Good explanations help people understand hateful memes and mitigate sharing. While AI-enabled automatic detection has proliferated, we argue that quality-controlled crowdsourcing can be an effective strategy to offer good explanations for hateful memes. This paper proposes a Generate-Annotate-Revise workflow to crowdsource explanations and presents the results from two user studies. Study 1 evaluated the objective quality of the explanation with three measurements: detailedness, completeness, and accuracy, and suggested that the proposed workflow generated higher quality explanations than the ones from a single-stage workflow without quality control. Study 2 used an online experiment to examine how different explanations affect users' perception. The results from 127 participants demonstrated that people without prior cultural knowledge gained significant perceived understanding and awareness of hateful memes when presented with explanations generated by the proposed multi-stage workflow as opposed to single-stage or machine-generated explanations.

Understanding and debugging of data structures and algorithms (DSA) is one of the most common tasks in computer science. DSA tests have also become a standard threshold that software developers have to cross to "get the job". One major challenge in comprehending and debugging DSA implementations lies in establishing and maintaining mental models of the quintessentially complex and twisted networks of events that make up their dynamic runtime behavior. Despite the high level of difficulty of this crucial task, general purpose tools to help users understand or reason about DSA implementations still have very limited capabilities. In this work we present Dbux-PDG, a dynamic Program Dependency Graph extension for the Dbux omniscient debugger. It captures data and control flow, as well as data dependencies of a program’s execution for visualization and user interaction. To deal with the immense complexity of non-trivial programs, it offers multiple layers of summarization, that allow the user to explore either the graph as a whole or in parts, one step at a time, as they see fit. We present our findings from applying Dbux-PDG to 94 diverse algorithms and explore its utility in several case studies. All visual results are made available in an online gallery. Dbux-PDG is open source and one-click installable, making it a powerful, easy-to-use tool prototype for DSA comprehension.Video URL: https://youtu.be/dgXj3VoQJZQ

Question-answering systems are becoming increasingly popular in Natural Language Processing, especially when applied in smart factory settings. A common practice in designing those systems is through intent classification. However, in a multiple-stage task commonly seen in those settings, relying solely on intent classification may lead to erroneous answers, as questions rising from different work stages may share the same intent but have different contexts and therefore require different answers. To address this problem, we designed an interactive dialogue system that utilizes contextual information to assist intent classification in a multiple-stage task. Specifically, our system incorporates user’s utterances with real-time video feed to better situate users’ questions and analyze their intent.

Image paragraph captioning aims to automatically generate a paragraph from a given image. It is an extension of image captioning in terms of generating multiple sentences instead of a single one, and it is more challenging because paragraphs are longer, more informative, and more linguistically complicated. Because a paragraph consists of several sentences, an effective image paragraph captioning method should generate consistent sentences rather than contradictory ones. It is still an open question how to achieve this goal, and for it we propose a method to incorporate objects' spatial coherence into a language-generating model. For every two overlapping objects, the proposed method concatenates their raw visual features to create two directional pair features and learns weights optimizing those pair features as relation-aware object features for a language-generating model. Experimental results show that the proposed network extracts effective object features for image paragraph captioning and achieves promising performance against existing methods.

Designing future IoT ecosystems requires new approaches and perspectives to understand everyday practices. While researchers recognize the importance of understanding social aspects of everyday objects, limited studies have explored the possibilities of combining data-driven patterns with human interpretations to investigate emergent relationships among objects. This work presents Thing Constellation Visualizer (thingCV), a novel interactive tool for visualizing the social network of objects based on their co-occurrence as computed from a large collection of photos. ThingCV enables perspective-changing design explorations over the network of objects with scalable links. Two exploratory workshops were conducted to investigate how designers navigate and make sense of a network of objects through thingCV. The results of eight participants showed that designers were actively engaged in identifying interesting objects and their associated clusters of related objects. The designers projected social qualities onto the identified objects and their communities. Furthermore, the designers changed their perspectives to revisit familiar contexts and to generate new insights through the exploration process. This work contributes a novel approach to combining data-driven models with designerly interpretations of thing constellation towards More-Than Human-Centred Design of IoT ecosystems.

Facial Action Unit (AU) detection is an important task to enable the emotion recognition from facial movements. In this paper, we propose a novel algorithm which utilizes identity-labeled face images to tackle the identity-based intra-class variation of AU detection that the appearances of the same AU vary significantly among different subjects, which makes existing methods generate low performance under cross-domain scenarios in case that the training and test datasets are dissimilar. The proposed method is based on network cascades consisting of two sub-tasks, face clustering and AU detection. The face clustering network, trained from a large dataset containing numerous identity-annotated face images, is designed to learn a transformation to extract identity-dependent image features, which are used to predict AU labels in the second network. The cascades are jointly trained by AU- and identity-annotated datasets that contain numerous subjects to improve the method's applicability. Experimental results show that the proposed method achieves state-of-the-art AU detection performance on benchmark datasets BP4D, UNBC-McMaster, and DISFA.

We develop a system which generates summaries from seniors' indoor-activity videos captured by a social robot to help remote family members know their seniors' daily activities at home. Unlike the traditional video summarization datasets, indoor videos captured from a moving robot poses additional challenges, namely, (i) the video sequences are very long (ii) a significant number of videoframes contain no-subject or with subjects at ill-posed locations and scales (iii) most of the well-posed frames contain highly redundant information. To address this problem, we propose to exploit pose estimation for detecting people in frames. This guides the robot to follow the user and capture effective videos. We use person identification to distinguish a target senior from other people. We also make use of action recognition to analyze seniors' major activities at different moments, and develop a video summarization method to select diverse and representative keyframes as summaries.

Parent-child communication is an essential element in behavioral and character development in early childhood; however, parents may find it difficult to be aware of how they talk to their children. Through extensive field studies with experts and parents, we found that parents are more likely to experience communication conflict with preschool children (3-6 years old) on school mornings. In consultation with domain professionals and families, we designed WAKEY, a technology-based approach that helps parents use better communication strategies to teach preschool children to carry out their morning routines. Following the intervention with WAKEY, parents reported significantly reduced levels of frustration during morning routines and greater independent behavior by children. Furthermore, parents reported experiencing changes in their parenting attitudes and finding new insights into communication.

Writing is challenging, especially for non-native speakers. To support English as a Second Language (ESL) writing, we propose StructFeed, which allows native speakers to annotate topic sentence and relevant keywords in texts and generate writing hints based on the principle of paragraph unity. First, we compared our crowd-based method with three naive machine learning (ML) methods and got the best performance on the identification of topic sentence and irrelevant sentence in the article. Next, we evaluated the StructFeed system with two feedback-generation mechanisms including feedback generated by one expert and by one crowd worker. The results showed that people who received feedback by StructFeed got the highest improvement after revision.

Data pertaining to the availability of parking slots is crucial to the efficient operation of systems designed to monitor the state of parking spaces. Outdoor parking systems have been developed using wireless sensors, Internet of Things (IoT) technology, and cameras. Unfortunately, interference from electromagnetic fields complicates the tuning of parameters for detection algorithms and limits accuracy to only 90 percent. In this study, we investigated these problems by collecting data from magnetic sensors, light sensors, and LoRa wireless modules used in the detection transient events (car arrivals and departures) over a period of 13 months. This led to the design an adaptive occupancy detection system using a variety of sensors, which can be deployed with only minimal calibration.

Building contextual models for new “smart” environments is not considered cost effective if data for model training must be collected from scratch. It is more practical to transfer as much learned knowledge as possible from an existing environment to the new target environment in order to reduce the data collection effort. In order to reuse learned knowledge from an original environment, this study proposed a feature-based knowledge transfer framework. The framework makes use of transfer learning, which relaxes the constraint requiring model training and testing datasets to be highly similar in distribution. Experimental results show that this framework can successfully help extract and transfer knowledge between two different smart-home environments. Models trained via the proposed framework can even outperform nontransfer-learning models by up to 8% in accuracy. Finally, the flexibility of the proposed framework enables used as a test bed for evaluating different methods and models in order to improve the service quality of human-centric context-aware applications.

Datasets gathered from sensor networks often suffer from a significant fraction of missing data, due to issues such as communication and sensor interference, power depletion, and hardware failure. Many standard data analysis tools such as classification engines, time-sequence pattern analysis modules, and statistical tools are ill-equipped to deal with missing values - hence, there is a vital need for highly-accurate techniques for imputing missing readings prior to analysis. This paper presents novel imputation methods that take a "recommendation systems" view of the problem: the sensors and their readings at each time step are viewed as products and user product ratings, with the goal of estimating the missing ratings. Sensor readings differ from product ratings, however, in that the former exhibit high correlation in both time and space. To incorporate this property, we modify the widely successful matrix factorization approach for recommendation systems to model inter-sensor and intra-sensor correlations and learn latent relationships among these dimensions. We evaluate the approach using two sensor network datasets, one indoor and one outdoor, and two imputation scenarios, corresponding to intermittent readings and failed sensors. Next, we consider sensor networks with multiple sensor types at each node. We present two techniques for extending our model to account for possible correlations among sensor types (e.g., temperature and humidity) with promising results. Finally, we study how the imputed values affect the result of data analysis. We consider a popular data analysis task - building regression-based prediction models - and show that, compared to prior approaches for imputation, our method leads to a much higher quality prediction model.

The need to find parking contributes to road congestion and leads to unnecessary fuel consumption. Of all emerging parking systems, Internet-of-Things (IoT)-based systems have demonstrated the feasibility of real-time delivery of parking availability using magnetic sensors. However, existing magnetic-based methods are prone to false positives caused by electromagnetic fields emitted from surrounding electric facilities. In this study, we conducted a 3-month data collection in a parking area. We identified the need to introduce learning and collaboration into the design of our detection algorithm which recognizes learned patterns associated with car arrivals or departures, and to filter out unreliable events based on spatial and temporal features.

The Reflexive Printer is a physical artifact combined with a mobile application. It allows digital-photo natives to enrich their experiences of daily reminiscence. Each day, the system takes one picture from a user's smartphone album, prints it on thermal paper as a halftone image, and deletes it from the smartphone. With a critical lens, we reframe technology-mediated reminiscence as an intersubjective interaction between human and artifact. In this mutually informed relationship, we propose perceived drawbacks as a design quality for provoking the critical sensibilities of users and engaging them in transgressing the normality of digital photo consumption. We focus our design thinking on three themes: simple materiality and monological performance, fast consumption and slow rumination, and powerful artifact and feeble user. This paper describes the initial lessons that we have learned through this critical making process and highlights several insights that HCI communities can leverage in the future.

A Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantical clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we recruited adults and children to perform the clustering task. Experimental results indicate that our computational model achieved a similar level of performance as children.

Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because of the subculture among the community is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system leveraging crowdsourcing technique to generate explanations for meme images. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes through reading the explanations. Our template-based explanation can illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by going through 2 designed humor tasks. In our pilot study, acceptable explanations for 5 unique memes are generated. For further study, generating explanations for more general text jokes are possible.

Activity recognition is a key capability for a smart environment to offer timely services and intelligent interactions with people, especially with the growing number of connected devices. While logging data from connected sensors is no longer beyond reach, it is still quite difficult to collect the labels required by machine learning approaches to activity recognition. In this research, crowdsourcing agents are designed to acquire status labels from people situated in the environment. Experiments on crowdsourcing in a typical building on campus have been conducted to improve air conditioning and space utilization. In particular, we will discuss how crowdsourcing agents in the form of simple physical objects can significantly improve user engagement as well as data quality. Collaboration among cyber-physical agents can lead to better user experience and overall performance.

Lingo is an emerging language on the Internet. Providing a standardized definition remains difficult due to continuous changes made to its nature. We proposed Tranzzl!n9o, a crossword puzzle game for engaging crowds to translate Internet lingo. Players provide explanations for lingo in parallel and iteratively verify the explanations from other players. Crowd-sourced translations are very informative containing explanations as well as lingo usage.

Effective space monitoring helps improve utilization of seminar rooms in the work place. This paper presents CrowdButtons as a simple solution to real-time space monitoring by crowdsourcing. CrowdButtons are tangible reporting devices designed to collect room status data from passersby. Anyone may participate in the micro QA task instantly by clicking a button, while the dashboard displays real-time room status as well as visualization of space usage patterns over time.

Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because the community subculture is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system that leverages crowdsourcing techniques to generate explanations for memes. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes by reading the explanations. Our template-based explanations illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by completing the two proposed human task processes. Experimental results suggest that the explanations produced by our system greatly help newcomers to understand unfamiliar memes. For further research, it is possible to employ our explanation generation system to improve computational humanities.

Learning a second language is becoming a more popular trend around the world. But the act of learning another language in a place removed from native speakers is difficult as there is often no one to correct mistakes nor examples to imitate. With the idea of crowd sourcing, we would like to propose an efficient way to learn a second language better.

The world is seeing more sensing and actuating devices deployed in our environment as part of the global digital ecosystem. One issue for perpetually running Internet of Things (IoT) devices is the energy efficiency. Many new IoT devices are running on powerful platforms that have ample computing and memory capacities to support multiple services. One energy saving strategy is therefore to co-locate several services on one device in order to reduce the computing and communication energy cost. Our research proposes the service merging approach for mapping and co-locating many services on one device. The service co-location problem is modeled as the Maximum Weighted Independent Set (MWIS) problem. We study the algorithms to transform a service flow to a co-location graph, and then use heuristic algorithms to find the maximum independent set which will be used for the service co-location decisions. The performance of different co-location algorithms are evaluated by simulation in this study.

Recommender systems are useful tools that help people to filter and explore massive information. While the accuracy of recommender systems is important, many recent research indicated that focusing merely on accuracy not only is insufficient to meet user needs, but also may be harmful. Other characteristics such as novelty, unexpectedness and diversity should also be taken into consideration. Previous work has shown that more the sales of long-tail items could be more beneficial to both customers and some business models. However, the majority of collaborative filtering approaches tends to recommend popular selling items.

Intelligent user interfaces require common sense knowledge to bridge the gap between the functionality of applications and the user’s goals. While current reasoning methods have been used to provide contextual information for interface agents, the quality of their reasoning results is limited by the coverage of their underlying knowledge bases. This article presents reasoning composition , a planning-based approach to integrating reasoning methods from multiple common sense knowledge bases to answer queries. The reasoning results of one reasoning method are passed to other reasoning methods to form a reasoning chain to the target context of a query. By leveraging different weak reasoning methods, we are able to find answers to queries that cannot be directly answered by querying a single common sense knowledge base. By conducting experiments on ConceptNet and WordNet, we compare the reasoning results of reasoning composition, directly querying merged knowledge bases, and spreading activation. The results show an 11.03% improvement in coverage over directly querying merged knowledge bases and a 49.7% improvement in accuracy over spreading activation. Two case studies are presented, showing how reasoning composition can improve performance of retrieval in a video editing system and a dialogue assistant.

Knowledge acquisition is the essential process of extracting and encoding knowledge, both domain specific and commonsense, to be used in intelligent systems. While many large knowledge bases have been constructed, none is close to complete. This paper presents an approach to improving a knowledge base efficiently under resource constraints. Using a guiding knowledge base, questions are generated from a weak form of similarity-based inference given the glossary mapping between two knowledge bases. The candidate questions are prioritized in terms of the concept coverage of the target knowledge. Experiments were conducted to find questions to grow the Chinese ConceptNet using the English ConceptNet as a guide. The results were evaluated by online users to verify that 94.17% of the questions and 85.77% of the answers are good. In addition, the answers collected in a six-week period showed consistent improvement to a 36.33% increase in concept coverage of the Chinese commonsense knowledge base against the English ConceptNet.

Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogues presents special challenges. To model the conversational dynamics of concurrent chatting behaviors, this article advocates Factorial Conditional Random Fields (FCRFs) as a model to accommodate co-temporal relationships among multiple activity states. In addition, to avoid the use of inefficient Loopy Belief Propagation (LBP) algorithm, we propose using Iterative Classification Algorithm (ICA) as the inference method for FCRFs. We designed experiments to compare our FCRFs model with two dynamic probabilistic models, Parallel Condition Random Fields (PCRFs) and Hidden Markov Models (HMMs), in learning and decoding based on auditory data. The experimental results show that FCRFs outperform PCRFs and HMMs-like models. We also discover that FCRFs using the ICA inference approach not only improves the recognition accuracy but also takes significantly less time than the LBP inference method.

In this paper, we explore the challenges in applying and investigate methodologies to improve direct-touch interaction on intangible displays. Direct-touch interaction simplifies object manipulation, because it combines the input and display into a single integrated interface. While traditional tangible display-based direct-touch technology is commonplace, similar direct-touch interaction within an intangible display paradigm presents many challenges. Given the lack of tactile feedback, direct-touch interaction on an intangible display may show poor performance even on the simplest of target acquisition tasks. In order to study this problem, we have created a prototype of an intangible display. In the initial study, we collected user discrepancy data corresponding to the interpretation of 3D location of targets shown on our intangible display. The result showed that participants performed poorly in determining the z-coordinate of the targets and were imprecise in their execution of screen touches within the system. Thirty percent of positioning operations showed errors larger than 30mm from the actual surface. This finding triggered our interest to design a second study, in which we quantified task time in the presence of visual and audio feedback. The pseudo-shadow visual feedback was shown to be helpful both in improving user performance and satisfaction.

This paper presents a programmable infrared (IR) technique that utilizes invisible, programmable markers to support interaction beyond the surface of a diffused-illumination (DI) multi-touch system. We combine an IR projector and a standard color projector to simultaneously project visible content and invisible markers. Mobile devices outfitted with IR cameras can compute their 3D positions based on the markers perceived. Markers are selectively turned off to support multi-touch and direct on-surface tangible input. The proposed techniques enable a collaborative multi-display multi-touch tabletop system. We also present three interactive tools: i-m-View, i-m-Lamp, and i-m-Flashlight, which consist of a mobile tablet and projectors that users can freely interact with beyond the main display surface. Early user feedback shows that these interactive devices, combined with a large interactive display, allow more intuitive navigation and are reportedly enjoyable to use.

Previous studies have outlined the advantages of multi-resolution large-area displays over their fixed-resolution counterparts, however the mobility of the focus region has up until the present time received little attention. To study this phenomenon further, we have developed a multi-resolution tabletop display system with a steerable high resolution focus region to compare the performance between steerable and fixed focus region systems under different working scenarios. We have classified these scenarios according to region of interest (ROI) with analogies to different eye movement types (fixed, saccadic, and pursuit ROI). Empirical data gathered during the course of a multi-faceted user study demonstrates that the steerable focus region system significantly outperforms the fixed focus region system. The former is shown to provide enhanced display manipulation and proves especially advantageous in cases where the user must maintain spatial awareness of the display content as is the case in which, within a single session, several regions of the display are to be visited.

Crystal balls are generally considered as media to perform divination or fortune-telling. These imaginations are mainly from some fantasy films and fiction, in which an augur can see into the past, the present, or the future through a crystal ball. With the distinct impressions, crystal ball has revealed itself as a perfect interface for the users to access and to manipulate visual media in an intuitive, imaginative and playful manner. We developed an interactive visual display system named Magic Crystal Ball (MaC Ball). MaC Ball is a spherical display system, which allows the users to see a virtual object/scene appearing inside a transparent sphere, and to manipulate the displayed content with barehanded interactions. Interacting with MaC Ball makes the users feeling acting with magic power. With MaC Ball, user can manipulate the display with touch and hover interactions. For instance, the user waves hands above the ball, causing clouds blowing from bottom of the ball, or slides fingers on the ball to rotate the displayed object. In addition, the user can press single finger to select an object or to issue a button. MaC Ball takes advantages on the impressions of crystal balls, allowing the users acting with visual media following their imaginations. For applications, MaC Ball has high potential to be used for advertising and demonstration in museums, product launches, and other venues.

Semantic-level content analysis is a crucial issue to achieve efficient content retrieval and management. In this paper, we propose an hierarchical approach that models the statistical characteristics of several audio events over a time series to accomplish semantic context detection. Two stages, including audio event and semantic context modeling/testing, are devised to bridge the semantic gap between physical audio features and semantic concepts. HMM are used to model audio events, and SVM and GMM are used to fuse the characteristics of various audio events related to some specific semantic concepts. The experimental results show that the approach is effective in detecting semantic context. The comparison between SVM- and GMM-based approaches is also studied

Personal experience computing is about computing support for recording, storing, retrieving, editing, analyzing, and sharing of personal experiences. In this paper, we present our design, implementation and evaluation of a mobile authoring tool called mProducer. mProducer enables a user to generate personal experience content using a mobile device anytime, anywhere. To address challenges in both limited system resources and user interface constraints on a mobile device, mProducer provides several innovative system techniques and UI designs. (1) The Storage Constrained Uploading (SCU) algorithm uploads large multimedia data to remote servers, in order to alleviate the problem of limited storage on a mobile device. (2) Sensor-Assisted Automated Editing utilizes a tilt sensor on the mobile device to automate the detection and removal of blurry frames resulting from excessive amount of camera shaking. This sensor-based solution requires small processing overhead, and it is considered a good alternative to computational-expensive image processing techniques for detecting shaking artifacts. (3) Map-based content management interface incorporates a GPS receiver on a mobile device to record location meta-data for each recording captured by a user, and enables easy, intuitive content navigation on a small screen. (4) Keyframe-based editing enables a user to edit content using only keyframes. We have conducted user studies to evaluate overall editing experience, user satisfaction in the editing quality, task performance time, ease-of-use, and learnability. The results of user studies have shown that keyframe-based editing works best with a storyboard interface. In general, users have found mProducer to be both fun and easy to use on a mobile device.

As the web becomes an increasingly important source of information, tools for modeling, searching, and extracting information from Web pages are indispensable. By modeling the structure of a Web page defined by its markup tags, one can easily extract target information using structural templates. This paper introduces the Tree Template Automatic Generator (TTAG) that learns tree-structured templates from training Web pages. TTAG was applied to both query-based and frequently updated Web sites, and produced effective templates from a small number of examples. The experiments show that TTAG is a powerful extraction tool for semi-structured information sources.

This paper presents a novel approach to solving the single-vehicle pickup and delivery problem with time windows and capacity constraints. While dynamic programming has been used to find the optimal routing to a given problem, it requires time exponential in the number of tasks. Therefore, it often fails to find the solutions under real-time conditions in an automated factory. This research explores anytime problem solving using genetic algorithms. By utilizing optimal but possibly partial solutions from dynamic programming, the hybrid genetic algorithms can produce near-optimal solutions for problems of sizes up to 25 percent bigger than what can be solved previously. This paper reports the experimental results of the proposed hybrid approach with four different crossover operators as well as three mutation operators. The experiments demonstrated the advantages of the hybrid approach with respect to dynamic task requests.

Presents a provably complete strategy for indoor environment exploration by an autonomous mobile robot. Without prior knowledge about the environment, the strategy guarantees the construction of a grid-based map, of the entire reachable area within a bounded region. Multiple map representations are utilized including a topological grid map for guiding the exploration process, a modified occupancy grid for fusing data from multiple range sensors, and a hierarchy of grids for real-time navigation. Experiments using a Nomad 200/sup TM/ robot have shown accurate map construction while navigating at a steady speed of 0.2m/sec.

The paper presents the design and implementation of PHYSIMC, which supports case based learning of elementary physics in a computer assisted simulation environment. The PHYSIMC system facilitates physics problem solving by providing: 1) a user friendly interface for problem specification via direct manipulation of physical objects; 2) 2D motion simulation of primitive physical objects; 3) a case library of successful problem solving episodes; and 4) a browsing tool for relevant problems and their corresponding solutions. As a result, a student can make use of past problem solving experiences in attempting to solve a new problem. In a knowledge based simulation environment, such case based learning tools help narrow the gaps due to incomplete domain knowledge.

This paper proposes a methodology for developing multi-agent systems. Several design considerations for robotic agents are discussed. A script specifies a multi-agent system by defining the behavior of each agent. An agent is modeled by an extended finite state automaton. By using the state transition rules as the building blocks, an agent as well as a multi-agent system can be developed efficiently. This methodology is further applied to multi-agent robotic systems in which reactivity and interaction are important issues. A script-based architecture is developed to implement the agents in multi-agent robotic systems. This architecture considers not only the control architecture of a single agent, but also the interactions among multiple agents and their environments. A script for the object-sorting task problem is utilized for demonstration.

In this paper, firstly we discuss the architecture of a flexible automated production system under which all components are well-modularized. Then, a solution, EMFAK (event-driven multi-tasking flexible automation kernel) is proposed to speed up the construction of system control. Finally, a flexible assembly system using EMFAK is described to show how to apply it to a real flexible automated production system.

This paper proposes a cooperation protocol based on the cost-balanced strategy for the Object-Sorting Task in multi-agent robotic systems. The protocol coordinates agents for carrying objects to destinations efficiently and effectively. Each agent autonomously makes subjective optimal decision, then the coordination algorithm resolves their conflicts by balancing the load, which is measured in terms of cost. Since coordination can be performed simultaneously with agent movement, it incurs very little overhead. The protocol is efficient because every agent runs the same algorithm to obtain the common results without further communication. Implementation of the protocol is realized on a distributed modular agent architecture for design simplicity, flexibility, and reactivity. Experimental results have shown that (1) the protocol has better performance than a previously proposed help-based cooperation protocol, (2) the protocol is flexible, and (3) the protocol can effectively utilize the agent power to achieve linear and superlinear speedup in most cases.

This paper proposes a coordination-based cooperation protocol for the object-sorting task in multi-agent robotic systems. The protocol coordinates the agents to move objects to destinations efficiently and effectively. Every agent autonomously makes subjective optimal decision, then the coordination algorithm resolves their conflicts by considering the global results. Since every agent runs the same algorithm to obtain the common results without further communication, the protocol is efficient. Implementation of the protocol is realized on a distributed modularized agent architecture. Experimental results shows that the protocol is effective and better than a previous proposed help-based protocol. In addition, its performance is very close to a high complexity model, genetic algorithms. The protocol can be applied to coordinate multiple autonomous robots for dispatching and transporting components in manufacturing systems.

This paper presents a multi-strategy learning technique for automatic generation of fuzzy control rules. In order to eliminate irrelevant input variables and to prioritize relevant ones according to their influences on the output value(s), the ID3 algorithm is adopted to classify the given set of training I/O data. The resulting decision tree can be easily converted into IF-THEN rules, which are then fuzzified. The fuzzy rules are further improved by tuning the parameters that define their membership functions using the gradient-descent approach. Experimental results of applying the proposed technique to nonlinear system identification have shown improvements over previous work in the area. In addition, it has been successfully applied to mobile robot control in unknown environments.

This paper presents a deadlock-free cooperation protocol for an object-sorting task in a multi-agent system. The object-sorting task in a distributed robotic system is introduced and a cooperation protocol for the task along with the agent architecture is proposed. The agents are based on a homogeneous agent architecture that consists of search, motion, and communication modules coordinated through a global state. The deadlock problem for the object-sorting task is addressed and several deadlock-handling strategies are provided to guarantee the cooperation protocol is deadlock-free.

The paper proposes a decentralized cooperation protocol for an object sorting task in a distributed robotic system. The multiple agents are based on a homogeneous agent architecture that consists of search, motion, and communication modules coordinated through a global state. Advantages of the system architecture are simplicity, intra agent distributed control no explicit inter agent control and flexibility. The protocol encourages the agents to help each other in order to facilitate overall task achievement. Simulation results showed that: the protocol is stable and reliable under a spectrum of workload; the execution time increases linearly with the number of objects; increasing the number of agents can significantly decrease the waiting time and improve the performance.>