Network telescopes or “Darknets” received unsolicited Internet-wide traffic, thus providing a unique window into macroscopic Internet activities associated with malware propagation, denial of service attacks, network reconnaissance, misconfigurations and network outages. Analysis of the resulting data can provide actionable insights to security analysts that can be used to prevent or mitigate cyber-threats. Large network telescopes, however, observe millions of nefarious scanning activities on a daily basis which makes the transformation of the captured information into meaningful threat intelligence challenging. To address this challenge, we present a novel framework for characterizing the structure and temporal evolution of scanning behaviors observed in network telescopes. The proposed framework includes four components. It (i) extracts a rich, high-dimensional representation ofscanning profilescomposed of features distilled from network telescope data; (ii) learns, in an unsupervised fashion, information-preservingsuccinct representationsof these scanning behaviors usingdeep representation learningthat is amenable to clustering; (iii) performsclusteringof the scanner profiles in the resulting latent representation space on daily Darknet data, and (iv)detects temporal changesin scanning behavior using techniques fromoptimal mass transport. We robustly evaluate the proposed system using both synthetic data and real-world Darknet data. We demonstrate its ability to detect real-world, high-impact cybersecurity incidents such as the onset of the Mirai botnet in late 2016 and several interesting cluster formations in early 2022 (e.g., heavy scanners, evolved Mirai variants, Darknet “backscatter” activities, etc.). Comparisons with state-of-the-art methods showcase that the integration of the proposed features with the deep representation learning scheme leads to better classification performance of Darknet scanners.

Network telescopes provide a unique window into Internet-wide malicious activities associated with malware propagation, denial of service attacks, network reconnaissance, and others. Analyses of this telescope data can highlight ongoing malicious events in the Internet which can be used to prevent or mitigate cyber-threats in real-time. However, large telescopes observe millions of events on a daily basis which renders the task of transforming this knowledge to meaningful insights challenging. In order to address this, we present a novel framework for characterizing Internet's background radiation and for tracking its temporal evolution. The proposed framework: (i) Extracts a high dimensional representation of telescope scanners composed of features distilled from telescope data and learns an information-preserving low-dimensional representation of these events that is amenable to clustering; (ii) Performs clustering of resulting representation space to characterize the scanners and (iii) Utilizes the clustering outcomes as "signatures" to detect temporal changes in the network telescope.

Enforcing a variety of security measures (such as intrusion detection systems, and so on) can provide a certain level of protection to computer networks. However, such security practices often fall short in face of zero-day attacks. Due to the information asymmetry between attackers and defenders, detecting zero-day attacks remains a challenge. Instead of targeting individual zero-day exploits, revealing them on an attack path is a substantially more feasible strategy. Such attack paths that go through one or more zero-day exploits are called zero-day attack paths. In this paper, we propose a probabilistic approach and implement a prototype system ZePro for zero-day attack path identification. In our approach, a zero-day attack path is essentially a graph. To capture the zero-day attack, a dependency graph named object instance graph is first built as a supergraph by analyzing system calls. To further reveal the zero-day attack paths hidden in the supergraph, our system builds a Bayesian network based upon the instance graph. By taking intrusion evidence as input, the Bayesian network is able to compute the probabilities of object instances being infected. Connecting the high-probability-instances through dependency relations forms a path, which is the zero-day attack path. The experiment results demonstrate the effectiveness of ZePro for zero-day attack path identification.

Online health communities constitute a useful source of information and social support for patients. American Cancer Society's Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.

People play different roles in various social networks. Even in a single network, people may interact with others based on different roles, and there are various relationships among them. However, current research usually treats all relationships homogeneously (i.e. friendship). In this paper, we try to identify different types of relationship (family, colleague, and social) within social networks. By analyzing a large-scale cellphone network, we gain insights about human mobility patterns. We design three metrics to capture colocation behaviors for cellphone users, taking spatial-temporal factors into consideration. These metrics show that users with different relationships demonstrate significantly different co-locating patterns. With these metrics as features, we adopt supervised approach to classify cellphone user pairs into different relationship categories. Comparing to using network and communication features, co-location metrics demonstrate better performance to fulfill the task of relationship identification.

The U.S. recently adopted a post-grant opposition procedure to encourage third parties to challenge the validity of newly granted patents by providing relevant prior patents that are missed during patent examination (i.e., missing citations). In this paper, we propose a recommendation system for missing citations for newly granted patents. The recommendation system, based on the patent citation network of a newly granted query patent, focuses on paths that start with the references of the query patent in the network. Our approach is to identify the relevancy of a candidate patent to the query patent by its citation relationship (paths) that are distinguished based on the direction, topology and semantics of the paths in the network. We consider six different types of paths between a candidate patent and a query patent based on their citation relationship and define a relevancy score for each path type. Accordingly, we rank candidate patents via a RankSVM model learned by using those relevancy scores as features. The experimental results show our approach significantly improves the average precision and recall performance compared to two baseline methods, i.e., Katz distance and text similarity.

introduction Free Access Share on Introduction to the Special Issue on Linking Social Granularity and Functions Authors: Qi He LinkedIn LinkedInView Profile , Juanzi Li Tsinghua University Tsinghua UniversityView Profile , Rong Yan Square SquareView Profile , John Yen Pennsylvania State University Pennsylvania State UniversityView Profile , Haizheng Zhang StarMerx LLC StarMerx LLCView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 5Issue 2Article No.: 22pp 1–3https://doi.org/10.1145/2594452Published:30 April 2014Publication History 2citation322DownloadsMetricsTotal Citations2Total Downloads322Last 12 Months4Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteeReaderPDF

Sentiment analysis has been widely researched in the domain of online review sites with the aim of getting summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users in online health communities such as cancer support forums, etc. Online health communities act as a medium through which people share their health concerns with fellow members of the community and get social support. Identifying sentiments expressed by members in a health community can be helpful in understanding dynamics of the community such as dominant health issues, emotional impacts of interactions on members, etc. In this work, we perform sentiment classification of user posts in an online cancer support community (Cancer Survivors Network). We use Domain-dependent and Domain-independent sentiment features as the two complementary views of a post and use them for post classification in a semi-supervised setting using the co-training algorithm. Experimental results demonstrate effectiveness of our methods.

Patent citation recommendation and prior patent search, critical for patent filing and patent examination, have become increasingly difficult due to the rapidly growing number of patents. Unlike paper citations that focus on reference comprehensiveness, patent citations tend to be more parsimonious and refer only to those prior patents bearing significant technological and/or economic value, as they define the scope of the citing patent and thus have significant legal and economic implications. Based on the insight that patent citations are important information reflecting the value of cited patents to the citing patent, we propose a heterogeneous patent citation-bibliographic network that combines patent citations (reflecting value relation) and bibliographic information (reflecting similarity relation) together. From this network, we extract various features that reflect the value of a prior patent to a query patent with regard to the context of the query patent such as its assignee, classifications, etc. We then propose a two-stage framework for patent citation recommendation. Our idea is that by exploiting those context-specific value measures of candidate patents to the query patent, the proposed framework is able to make effective patent citation recommendations. We evaluate the proposed context-guided value-driven framework using a collection of 1.8M U.S. patents. Experimental results validate our ideas and show that those value-driven features are very effective and significantly outperform two state-of-the-art methods in terms of both the precision and recall rates.

Cyber analysis is a difficult task for analysts due to huge amounts of noise-abundant monitoring data and increasing complexity of the reasoning tasks. Therefore, experience from experts can provide guidance for analysts' analytical reasoning and contribute to training. Despite its great potential benefits, experience has not been effectively leveraged in the existing reasoning support systems due to the difficulty of elicitation and reuse. To fill the gap, we propose an experience-aided reasoning support system which can automatically capture experts' experi-ence and subsequently guide the novices' reasoning in a step-by-step manner. Drawing on cognitive theory, we model experience as a reasoning process involving “actions”, “observations”, and “hypotheses”. Computability and adaptability are the compar-ative advantages of this model: the “hypotheses” capture analysts' internal mental reasoning as a black box, while the “actions” and “observations” formally representing the external context and analysts' evidence exploration activities. This paper demonstrates how this system, built on this experience model, can capture and utilize experience effectively.

Link prediction is one of central tasks in the study of social network evolution and has many applications. In this paper, we use time series to describe node behavior, extract temporal features from the time series to characterize behavior evolution of nodes, and use the temporal features for link prediction. Our experimental results on several real datasets suggest that including the temporal features developed in the paper significantly improve link prediction performance.

Determining a good feature set is critical to the performance of learning algorithms such as classifiers. Recently, researchers have proposed evolutionary-based feature extraction methods that aim to find a good feature set by combining the original features with new features generated by mathematical transformations of the original features. In this paper, we propose dynamically collecting past performance information on promising features and operators to use in our mutation method. We consider how to make our evolutionary algorithm more efficient and reliable by reducing overfitting. Preliminary results using UCI data show that our dynamic mutation method only slightly enhances the classification accuracy but it produces more reliable results.

Extracting good features is critical to the performance of learning algorithms such as classifiers. Feature extraction selects and transforms original features to find information hidden in data. Due to the huge search space of selection and transformation of features, exhaustive search is computationally prohibitive and randomized search such as evolutionary algorithms (EA) are often used. In our prior work on evolutionary-based feature extraction, an individual, which represents a set of features, is evaluated by estimating the accuracy of a classifier when the individual's feature set is used for learning. Although incorporating a learning algorithm during evaluation, which is called the wrapper approach, generally performs better than evaluating an individual simply by the statistical properties of data, which is called the filter appproach, our EA based on a wrapper approach suffers from overfitting, so that a slight enhancement of fitness in training can dramatically reduce the classification accuracy for unseen testing data. To cope with this problem, this paper proposes a two-population EA for feature extraction (TEAFE) that combines filter and wrapper approaches, and shows the promising preliminary results.

This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition.

Recent research on human-centered teamwork highly demands the design of cognitive agents that can model and exploit human partners' cognitive load to enhance team performance. In this paper, we focus on teams composed of human-agent pairs and develop a system called Shared Mental Models for all--SMMall. SMMall implements a hidden Markov model (HMM)-based cognitive load model for an agent to predict its human partner's instantaneous cognitive load status. It also implements a user interface (UI) concept called shared belief map, which offers a synergic representation of team members' information space and allows them to share beliefs. An experiment was conducted to evaluate the HMM-based load models. The results indicate that the HMM-based load models are effective in helping team members develop a shared mental model (SMM), and the benefit of load-based information sharing becomes more significant as communication capacity increases. It also suggests that multiparty communication plays an important role in forming/evolving team SMMs, and when a group of agents can be partitioned into subteams, splitting messages by their load status can be more effective for developing subteam SMMs.

In evolving social networks, nodes join, make connections, or leave over time. In this paper, we introduce node event sequences that record activities of every node over time. Node event sequences are suitable for microscopic analysis of node behaviors in social networks. As preliminary results of taking advantage of node event sequences (as well as snapshots of networks), we study the health of the community of Nanotechnology based on the analysis of Seniority of nodes, identify the intrinsic dynamics of formation of new edges and its relation to Seniority of nodes, and the changes in the node behavior according to the nodes' Seniority.

Naturalistic decision making (NDM) focuses on how people actually make decisions in realistic settings that typically involve ill-structured problems. Taking an experimental approach, we investigate the impacts of using an NDM-based software agent (R-CAST) on the performance of human decision-making teams in a simulated C3I (Communications, Command, Control and Intelligence) environment. We examined four types of decision-making teams with mixed human and agent members playing the roles of intelligence collection and command selection. The experiment also involved two within-group control variables: task complexity and context switching frequency. The result indicates that the use of an R-CAST agent in intelligence collection allows its team member to consider the latest situational information in decision making but might increase the team member's cognitive load. It also indicates that a human member playing the role of command selection should not rely too much on the agent serving as his or her decisi...

Abstract The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fast‐evolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific‐collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real‐world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance‐based computational network growth schemes, namely the distance‐based growth model (DG) and the hybrid degree and distance‐based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275,000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.

Multicontext team decision making under time stress is an extremely challenging issue faced by various real-world application domains. In this paper, we employ an experience-based cognitive agent architecture (called R-CAST) to address the informational challenges associated with military command and control (C2) decision-making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C2decision-making teams. Members from a U.S. Army Reserve Officer Training Corps organization were randomly recruited as human participants. They were grouped into ten human-human teams, each composed of two participants, and ten human-agent teams, each composed of one participant and two R-CAST agents, as teammates and decision aids. The statistical inference of experimental results indicates that R-CAST agents can significantly improve the performance of C2teams in multicontext decision making under varying time-stressed situations.

Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA (simple social network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA is that it only requires topological information as input. This model is evaluated on two research collaborative networkst: CtteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

Topic detection and tracking and topic segmentation play an important role in capturing the local and sequential information of documents. Previous work in this area usually focuses on single documents, although similar multiple documents are available in many domains. In this paper, we introduce a novel unsupervised method for shared topic detection and topic segmentation of multiple similar documents based on mutual information (MI) and weighted mutual information (WMI) that is a combination of MI and term weights. The basic idea is that the optimal segmentation maximizes MI (or WMI). Our approach can detect shared topics among documents. It can find the optimal boundaries in a document, and align segments among documents at the same time. It also can handle single-document segmentation as a special case of the multi-document segmentation and alignment. Our methods can identify and strengthen cue terms that can be used for segmentation and partially remove stop words by using term weights based on entropy learned from multiple documents. Our experimental results show that our algorithm works well for the tasks of single-document segmentation, shared topic detection, and multi-document segmentation. Utilizing information from multiple documents can tremendously improve the performance of topic segmentation, and using WMI is even better than using MI for the multi-document segmentation.

Helping behavior in effective teams is achieved via some overlapping "shared mental models" that are developed and maintained by members of the team. In this paper, we take the perspective that multiparty "proactive" communication is critical for establishing and maintaining such a shared mental model among teammates, which is the basis for agents to offer proactive help and to achieve coherent teamwork. We first provide formal semantics for multiparty proactive performatives within a team setting. We then examine how such performatives result in updates to mental model of teammates, and how such updates can trigger helpful behaviors from other teammates. We also provide conversation policies for multiparty proactive performatives.

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well-known human decision-making model called Recognition-Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.

Text segmentation is important for text analysis, while text alignment is to determine shared sub-topics among similar documents. Multi-task text segmentation and alignment is the extension of single-task segmentation to utilize information of multi-source documents. In this paper we introduce a novel domain-independent unsupervised method for multi-task segmentation and alignment based on the idea that the optimal segmentation and alignment maximizes weighted mutual information, mutual information with term weights. The experiment results show that our approach works well.

MALLET, a multi-agent logic language for encoding teamwork, is intended to enable expression of teamwork emulating human teamwork, allowing experimentation with different levels and forms of inferred team intelligence. A consequence of this goal is that the actual teamwork behavior is determined by the level of intelligence built into the underlying system as well as the semantics of the language. In this paper, we give the design objectives, the syntax, and an operational semantics for MALLET in terms of a transition system. We show how the semantics can be used to reason about the behaviors of team-based agents. The semantics can also be used to guide the implementation of various MALLET interpreters emulating different forms of team intelligence, as well as formally study the properties of team-based agents specified in MALLET. We have explored various forms of proactive information exchange behavior embodied in human teamwork using the CAST system, which implements a built-in MALLET interpreter.

Multiparty communication and proactive communication have each been studied extensively, but separately. A combinatorial approach would be to investigate proactive communication in a multiparty settings, aiming to support effective collaboration by leveraging the benefits of both. In this paper, we take a first attempt along this direction, providing formal definitions for multiparty proactive performatives. Such performatives not only result in an exchange of knowledge among teammates, but also in an increased awareness about teammates' mental states which can initiate additional helpful behaviors.

Keeping track of changing interests is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. Being able to do so with a few feedback examples poses an even more important and challenging problem because existing concept drift learning algorithms that handle the task typically suffer from it. This work presents a new computational framework for extending incomplete labeled data stream (FEILDS), which extends the capability of existing algorithms for learning concept drift from a few labeled data. The system transforms the original input stream into a new stream that can be conveniently tracked by the existing learning algorithms. The experiment results reveal that FEILDS can significantly improve the performances of a Multiple Three-Descriptor Representation (MTDR) algorithm, Rocchio algorithm, and window-based concept drift learning algorithms when learning from a sparsely labeled data stream with respect to their performances without using FEILDS.

Currently Web services composition problems are addressed using AI planning techniques. The team-based approach, with emphases on the sharing of mental models and proactive collaboration, provides an alternative to current static approaches to Web service composition. The approach provides clear advantages for proactive handling of failures that may be encountered during execution of a complex Web service. The paper proposes a generic framework for dynamic Web-service composition, and extends the CAST architecture to realize the framework.

Keeping track of changes in user interests from a document stream with a few relevance judgments is not an easy task. To tackle this problem, we propose a novel method that integrates (1) pseudo-relevance feedback mechanism, (2) assumption about the persistence of user interests and (3) incremental method for data clustering. This approach has been empirically evaluated using Reuters-21578 corpus in a setting for information filtering. The experiment results reveal that it significantly improves the performances of existing user-interest-tracking systems without requiring additional, actual relevance judgments.

How agents accomplish a goal task in a multi-agent system is usually specified by multi-agent plans built from basic actions (e.g. operators) of which the agents are capable. The plan specification provides the agents with a shared mental model for how they are supposed to collaborate with each other to achieve the common goal. Making sure that the plans are reliable and fit for the purpose for which they are designed is a critical problem with this approach. To address this problem, this paper presents a formal approach to modeling and analyzing multi-agent behaviors using Predicate/Transition (PrT) nets, a high- level formalism of Petri nets. We model a multi-agent problem by representing agent capabilities as transitions in PrT nets. To analyze a multi-agent PrT model, we adapt the planning graphs as a compact structure for reachability analysis, which is coherent to the concurrent semantics. We also demonstrate that one can analyze whether parallel actions specified in multi-agent plans can be executed in parallel and whether the plans can achieve the goal by analyzing the dependency relations among the transitions in the PrT model.

In this paper we present a novel incremental hierarchical clustering (IHC) algorithm. Our approach aims to construct a hierarchy that satisfies homogeneity and monotonicity properties. Working in a bottom-up fashion, a new instance is placed in the hierarchy and a sequence of hierarchy restructuring processes is performed only in regions that have been affected by the presence of the new instance. The experimental results on a variety of domains demonstrate that our algorithm is not sensitive to input ordering, can produce a quality cluster hierarchy, and is efficient in terms of computational time.

Psychological studies about human teamwork have shown that members of an effective team can often anticipate needs of other teammates, and choose to assist teammates through appropriate ways. However the fulfillment of help behaviors among teammates is subject to team members' understanding of the underpinning communicative acts performed by individual agents. By extending the mental state analysis of performatives initiated by Cohen and Levesque (1990), we focus this paper on analyzing a missing class of performatives -proactive performatives, which are prevalently used by individual agents to exchange information among teammates.

In a multi-agent system, how agents accomplish a goal task is usually specified by multi-agent plans built from basic actions (e.g. operators) of which the agents are capable. A critical problem with such an approach is how can the designer make sure the plans are reliable. To tackle this problem, this paper presents a formal approach for modeling and analyzing multi-agent behaviors using Predicate/Transition (PrT) nets, a high-level formalism of Petri nets. We construct a multi-agent model by representing agent capabilities as transitions. To verify a multi-agent PrT model, we adapt the planning graphs as a compact structure for the reachability analysis. We also demonstrate that, based on the PrT model, whether parallel actions specified in multi-agent plans can be executed in parallel and whether the plans guarantee the achievement of the goal can be verified by analyzing the dependency relations among the transitions.

The genetic algorithm is one of the commonly used approaches to data mining. We propose a genetic algorithm approach for classification problems. Binary coding is adopted in which an individual in a population consists of a fixed number of rules that stand for a solution candidate. The evaluation function considers four important factors which are error rate, entropy measure, rule consistency and hole ratio, respectively. Adaptive asymmetric mutation is applied by the self-adaptation of mutation inversion probability from 1-0 (0-1). The generated rules are not disjoint but can overlap. The final conclusion for prediction is based on the voting of rules and the classifier gives all rules equal weight for their votes. Based on three databases, we compared our approach with several other traditional data mining techniques including decision trees, neural networks and naive bayes learning. The results show that our approach outperformed others in both prediction accuracy and the standard deviation.

Query refinement can help users find information on the Internet more effectively. This feature has been implemented in a PASS (personalized abstract search services) system, a Web-based, domain-specific search engine for searching abstracts of research papers. The system uses a fuzzy ontology of term associations to support the feature. The ontology is automatically built in two stages using information obtained from the system's collection. A preliminary user study reveals that query refinement is one of the most important features of the system.

Learning users' interest categories is challenging in a dynamic environment like the Web because they change over time. This article describes a novel scheme to represent a user's interest categories, and an adaptive algorithm to learn the dynamics of the user's interests through positive and negative relevance feedback. We propose a three-descriptor model to represent a user's interests. The proposed model maintains a long-term interest descriptor to capture the user's general interests and a short-term interest descriptor to keep track of the user's more recent, faster-changing interests. An algorithm based on the three-descriptor representation is developed to acquire high accuracy of recognition for long-term interests, and to adapt quickly to changing interests in the short-term. The model is also extended to multiple three-descriptor representations to capture a broader range of interests. Empirical studies confirm the effectiveness of this scheme to accurately model a user's interests and to adapt appropriately to various levels of changes in the user's interests.

We present a fuzzy similarity approach to address a text classification problem. The effectiveness of several fuzzy conjunction and disjunction operators used in a fuzzy similarity formula and on several document representations were evaluated using test sets from three text document collections. Based on empirical results obtained from using these collections, a special case of the fuzzy similarity formula performs very well.

We put forward a neural network approach to predict existing and in-fill oil well performance. Multiple wells history production data were used to train the neural network, and the established neural network can be used to predict future performance of oil wells. No reservoir data is currently involved in the establishment of neural network, therefore it can predict well production performance in absence of reservoir data. Since both the static and dynamic data are used in the training, we combine the spatial and time series prediction together in this approach. Primary production of a 9-well area in North Robertson Unit located in west Texas was tested in this paper. The results demonstrate that our approach is powerful in rapid projection of existing wells future performance, as well as the performance prediction of in-fill drilling wells. By incorporating the appropriate optimization technique, it can be further extended for use in location optimization of in-fill drilling wells.

The ability to express emotions is important for creating believable interactive characters. To simulate emotional expressions in an interactive environment, an intelligent agent needs both an adaptive model for generating believable responses, and a visualization model for mapping emotions into facial expressions. Recent advances in intelligent agents and in facial modeling have produced effective algorithms for these tasks independently. We describe a method for integrating these algorithms to create an interactive simulation of an agent that produces appropriate facial expressions in a dynamic environment. Our approach to combining a model of emotions with a facial model represents a first step towards developing the technology of a truly believable interactive agent which has a wide range of applications from designing intelligent training systems to video games and animation tools.

Genetic Algorithms (GA's) have been demonstrated to be a promising search and optimization technique. However, there are two issues regarding applying genetic algorithms to complex system identifications. The first issue is the high computational cost due to their slow convergence. The second issue is its scalability to deal with high dimensional model identification problems. To alleviate the difficulties, we propose a two-layer supervisory model optimization architecture and hybrid GA algorithms. The upper supervisory layer guides the low level optimization algorithm so that the optimization space of the algorithm is gradually reduced. The lower layer uses simplex-GA approach to perform search and numerical optimization within the range defined by the upper layer. Simplex is added as an additional operator of traditional GA to speed up the convergence. We have applied the proposed approach to tomographic reconstruction and the modeling of central metabolism, the results are satisfactory.

In this paper, we describe a new scheme to learn dynamic user's interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.

Traditionally, fuzzy logic has been viewed in the artificial intelligence (AI) community as an approach for managing uncertainty. In the 1990s, however, fuzzy logic has emerged as a paradigm for approximating a functional mapping. This complementary modern view about the technology offers new insights about the foundation of fuzzy logic, as well as new challenges regarding the identification of fuzzy models. In this paper, we first review some of the major milestones in the history of developing fuzzy logic technology. After a short summary of major concepts in fuzzy logic, we discuss a modern view about the foundation of two types of fuzzy rules. Finally, we review some of the research in addressing various challenges regarding automated identification of fuzzy rule-based models.

An important issue in fuzzy-rule-based modeling is how to select a set of important fuzzy rules from a given rule base. Even though it is conceivable that removal of redundant or less important fuzzy rules from the rule base can result in a compact fuzzy model with better generalizing ability, the decision as to which rules are redundant or less important is not an easy exercise. In this paper, we introduce several orthogonal transformation-based methods that provide new or alternative tools for rule selection. These methods include an orthogonal least squares (OLS) method, an eigenvalue decomposition (ED) method, a singular value decomposition and QR with column pivoting (SVD-QR) method, a total least squares (TLS) method, and a direct singular value decomposition (D-SVD) method. A common attribute of these methods is that they all work on a firing strength matrix and employ some measure index to detect the rules that should be retained and eliminated. We show the performance of these methods by applying them to solving a nonlinear plant modeling problem. Our conclusions based on analysis and simulation can be used as a guideline for choosing a proper rule selection method for a specific application.

Conflict identification and resolution are inevitable parts of the requirement analysis process. Over the past few years, the need to deal with conflicting system requirements has become increasingly important. These requirements are often elastic in that they can be satisfied to a degree. The overall goal of this research is to develop a formal methodology that facilitates the identification and tradeoff analysis of conflicting requirements by explicitly capturing their elasticity. In order to capture the elasticity of imprecise requirements, we represent imprecise requirements using fuzzy logic. Based on the representation, we build a formal foundation to facilitate the identification of conflicting requirements. Once the conflicting requirements are identified, we describe a systematic approach for analyzing the tradeoff between conflicting requirements using the techniques in decision science. The systematic tradeoff analyses are used for three important tasks in the requirement engineering process: (1) for validating the structure used in aggregating prioritized requirements, (2) for assisting requirement engineers in identifying the structures and the parameters of the underlying representation of imprecise requirements and in eliciting them from the customer, and (3) for assessing the priorities of conflicting requirements. We illustrate the usage of these techniques using the requirements of a conference room scheduling system.

Theoretical studies have shown that fuzzy models are capable of approximating any continuous function on a compact domain to any degree of accuracy. However, constructing a good fuzzy model requires finding a good tradeoff between fitting the training data and keeping the model simple. A simpler model is not only easily understood, but also less likely to overfit the training data. Even though heuristic approaches to explore such a tradeoff for fuzzy modeling have been developed, few principled approaches exist in the literature due to the lack of a well-defined optimality criterion. In this paper, we propose several information theoretic optimality criteria for fuzzy models construction by extending three statistical information criteria: 1) the Akaike information criterion [AIC] (1974); 2) the Bhansali-Downham information criterion [BDIC] (1977); and 3) the information criterion of Schwarz (1978) and Rissanen (1978) [SRIC]. We then describe a principled approach to explore the fitness-complexity tradeoff using these optimality criteria together with a fuzzy model reduction technique based on the singular value decomposition (SVD). The role of these optimality criteria in fuzzy modeling is discussed and their practical applicability is illustrated using a nonlinear system modeling example.

The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multimodel approach in which simple submodels (typically linear models) are combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance (i.e., the model is capable of approximating the given system with arbitrary accuracy, provided that sufficient rules are used and sufficient training data are available), they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior, which is difficult to interpret. Since one of the important motivations of using the TSK model (also other fuzzy models) is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. We propose a new learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighed regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user's preference, generating models with good tradeoff in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example.

One of the main obstacles in applying genetic algorithms (GA's) to complex problems has been the high computational cost due to their slow convergence rate. We encountered such a difficulty in our attempt to use the classical GA for estimating parameters of a metabolic model. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques including a simplex-GA hybrid independently developed by Renders-Bersini (R-B) and adaptive simulated annealing (ASA). Our empirical evaluations showed that our hybrid approach for the metabolic modeling problem outperformed all other techniques in terms of accuracy and convergence rate. We used two additional function optimization problems to compare our approach with the five alternative methods.

The need to deal with conflicting system requirements has become increasingly important over the past several years. Often, these requirements are elastic in that they can be satisfied to a degree. The overall goal of this research is to develop a formal framework that facilitates the identification and the tradeoff analysis of conflicting requirements by explicitly capturing their elasticity. Based on a fuzzy set theoretic foundation for representing imprecise requirements, we describe a systematic approach for analyzing the tradeoffs between conflicting requirements using the techniques in decision science. The systematic tradeoff analyses are used for three important tasks in the requirement engineering process: (1) for validating the structure used in aggregating prioritized requirements, (2) for identifying the structures and the parameters of the underlying representation of imprecise requirements and (3) for assessing the priorities of conflicting requirements. We illustrate these techniques using the requirements of a conference room scheduling system.

We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer. It is shown that the modified RBF network can reduce the number of hidden units significantly. A computationally efficient algorithm, known as the expectation-maximization (EM) algorithm, is used to estimate the parameters of the regression weights. A salient feature of this algorithm is that it decomposes a complicated multiparameter optimization problem into L separate small-scale optimization problems, where L is the number of hidden units. The superior performance of the modified RB network over the standard RBF network is illustrated by computer simulations.

This paper proposes to use the method of principal components to reduce the dimensionality of input space and the B-splines to represent the membership functions of input variables. A model reduction strategy, which is based on Johansen’s optimality theorem, also suggested. The utility of this approach is illustrated using a fuzzy system modeling example.

Priority analysis is one of the most important issues in the trade-off analysis of imprecise conflicting requirements whose elasticity is captured using fuzzy logic. Requirement analysts need to know not only the relative ordering of requirements based on their importance but also how much one requirement is more important than another requirement in order to achieve an effective trade-off. This paper presents a formal approach for reasoning about relative priority by analysing a customer's trade-off preference among imprecise conflicting requirements. A possibilistic reasoning framework for inferring relative priority from case analysis under uncertainty is also developed.

Genetic algorithms (GA) have been demonstrated to be a promising search and optimization technique that is more likely to converge to a global optimum than most alternative techniques. In an attempt to apply GA to estimate parameters of a metabolic model, however, we found that the slow convergence rate of GA becomes a major problem for its applications to model identification of dynamic systems due to the high computational costs associated with the evaluation of models. To alleviate this difficulty, we developed a hybrid approach that combines Nelder and Mead's (1965) simplex method with the genetic algorithm. The hybrid approach not only speeds up GA's rate of convergence, but also improves the quality of the solution found by pure GA.

Elasticity in an imprecise requirement needs to be captured to enable the trade-off analysis of conflicting requirements. One of the most important issues in the trade-off analysis of conflicting requirements is to understand their priorities. Requirement analysts need to know not only the relative ordering of requirements based on their importance but also how much a requirement is more important than another requirement in order to achieve an effective trade-off between conflicting requirements. Existing formal methods for requirement engineering are limited in addressing these issues. This paper presents a formal methodology for reasoning about their priority by analyzing the customer’s trade-off preference among imprecise conflicting requirements. The elasticity in imprecise requirements is captured using fuzzy logic. Conflicting and cooperative relationships are classified to detect the conflicts between requirements. Multiple requirements are combined based fuzzy multi-criteria decision making techniques. We have also developed a possibilistic reasoning framework for inferring the lower bound of relative priority from case analysis. Consistency and nonredundancy criteria are established to facilitate the aggregation of possibilistic statement on the lower bounds of relative priority. Finally, we describe a process for transforming the lower bounds of relative priority into weights of importance so that they can be used in the aggregation of conflicting requirements to resolve conflicts.

Payton and Rosenblatt (1990) have proposed a command fusion method for combining outputs of multiple behaviors in a mobile robot navigation system such that information loss due to command fusion can be reduced. Using linguistic fuzzy rules to explicitly capture heuristics implicit in the Payton-Rosenblatt approach, we have extended their approach to a fuzzy logic architecture for mobile robot navigation in dynamic environments, which is simpler and easier to understand and modify. We have also developed and empirically tested a new defuzzification technique for alleviating difficulties in applying existing defuzzification methods to mobile robot navigation control.>

Knowledge acquisition has been identified as the bottleneck for knowledge engineering. One of the reasons is the lack of an integrated methodology that is able to provide tools and guidelines for the elicitation of knowledge as well as the verification and validation of the system developed. Even though methods that address this issue have been proposed, they only loosely relate knowledge acquisition to the remaining part of the software development life cycle. to alleviate this problem, we have developed a framework in which knowledge acquisition is integrated with system specifications to facilitate the verification, validation, and testing of the prototypes as well as the final implementation. to support the framework, we have developed a knowledge acquisition tool, TAME. It provides an integrated environment to acquire and generate specifications about the functionality and behavior of the target system, and the representation of the domain knowledge and domain heuristics. the tool and the framework, together, can thus enhance the verification, validation, and the maintenance of expert systems through their life cycles. © 1994 John Wiley & Sons, Inc.

Several methodologies have been developed to enhance the software life cycle of knowledge-based systems by emphasizing on the use of both prototypes and specifications. However, these methodologies focus on the development phase of knowledge-based systems. The roles of prototypes and specifications in the maintenance phase has not been fully explored. Because a suitable problem specification for a knowledge-based system is often difficult to acquire, validating changes to non-executable solution specification during the maintenance phase can be a problem. To address this, we propose an alternative paradigm in which the prototype complements the specification throughout the life cycle. The traceability between them is facilitated by organizing both types of artifacts using a common functional decomposition structure. Based on our task-based specification methodology (TBSM), we have also developed a knowledge engineering tool (called TAME) to facilitate the acquisition and the organization of the specification and the prototype. The proposed methodology and the tool together can thus enhance the verification, validation, and the maintenance of knowledge-based systems through their life cycles.

A knowledge acquisition framework in which both domain knowledge and specification elicited and refined can interact with a knowledge engineer is proposed. TAME, a hypertext-based knowledge acquisition assistant supporting the framework in an integrated environment, is described. TAME supports a task-based specification methodology (TBSM) in knowledge acquisition and specification elicitation in the following ways. First, TAME's templates provide the building blocks and autolinks in the acquisition process. Second, TAME's browsing and retrieval aids allow users to navigate in the knowledge document using search, navigation links, etc. Finally, TAME generates feedback to inform users about incomplete refinements, duplications, and inconsistent composition of task state expressions (TSEs).>

A general methodology for specifying both the model and the process knowledge of an expert system at different abstraction levels is proposed. Specifications are acquired and organized around the general notion of a task. The model specification of a task describes a partial model of the application domain and a partial model of the problem solving states relevant to the task. The process specification of a task describes states before, during, and after the task as well as task state expression to describe the behavior. A piece of abstract specification can be refined to a more detailed specification. Specifications at different abstraction levels can be verified for their consistency and completeness.>

In this panel session, the following topics are discussed: artificial intelligence in business; artificial intelligence in multimedia; neural networks as a tool for artificial intelligence: software engineering for knowledge-based systems: and artificial intelligence as a solution for software engineering.>

The general architecture and an implementation of a classification-based production system (CLASP) are presented. The main objective is to extend the benefits of classification capabilities in frame systems to the developers of rule-based systems. Two major processes of CLASP, a semantic pattern matcher and a pattern classifier, are described. The semantic pattern matcher extends the pattern matching capabilities of rule-based systems through the use of terminological knowledge. The pattern classifier enables the system to compute a rule's specificity, which is useful for conflict resolution, based on the semantics of its left-hand side. The paradigm not only enhances the reasoning capabilities of rule-based systems, but also helps to reduce the cost of maintaining such systems because definitional knowledge is explicitly represented in a form that facilitates sharing and minimizes duplication of effort.>

The task of maintaining expert systems has become increasingly difficult as the size of their knowledge bases increases. To address this issue, a unified AI programming environment (CLASP) has been developed; this environment tightly integrates three AI programming schemes: the term subsumption languages in knowledge representation the production system architecture, and methods in object-oriented programming. The CLASP architecture separates the knowledge about when to trigger a task from the knowledge about how to accomplish a given task. It also extends the pattern matching capabilities of conventional rule-based systems by using the semantic information related to rule conditions. In addition, it uses a pattern classifier to compute a principled measure about the specificity of rules. Using a monkey-bananas problem, the authors demonstrate that an expert system built in CLASP is easier to maintain because the architecture facilitates the development of a consistent and homogeneous knowledge base, enhances the predictability of rules, and improves the organization and reusability of knowledge.>

A generalization of the Dempster-Schafer (D-S) theory to deal with fuzzy sets is described in which the belief and plausibility functions are treated as lower and upper probabilities. It is shown that computing the degree of belief in a hypothesis in the D-S theory can be formulated as an optimization problem. The extended belief function is thus obtained by generalizing the objective function and the constraints of the optimization problem. To combine bodies of evidence that may contain vague information, Dempster's rule (1967) is extended by (1) combining generalized compatibility relations based on the possibility theory, and (2) normalizing combination results to account for partially conflicting evidence. The generalization not only extends the application of the D-S theory but also illustrates a way that probability theory and fuzzy set theory can be integrated in a sound manner in order to deal with different kinds of uncertain information in intelligent systems.>