Diverse planning is an important problem in automated planning with many real world applications. Recently, diverse planning has seen renewed interest, with work that defines a taxonomy of computational problems with respect to both plan quality and solution diversity. However, despite the recent advances in diverse planning, the variety of approaches and the number of available planners are still quite limited, even nonexistent for several computational problems. In this work, we aim to extend the portfolio of planners for various computational problems in diverse planning. To that end, we introduce a novel approach to finding solutions for three computational problems within diverse planning and present planners for these three problems. For one of these problems, our approach is the first one that is able to provide solutions to the problem. For another, we show that top-k and top quality planners can provide, albeit naive, solutions to the problem and we extend these planners to improve the diversity of the solution. Finally, for the third problem, we show that some existing diverse planners already provide solutions to that problem. We suggest another approach and empirically show it to compare favorably with these existing planners.

Scenario Planning is a commonly used Enterprise Risk Management (ERM) technique to help decision makers with longterm plans by considering multiple alternative futures. It is typically a manual, highly labor intensive process involving dozens of experts and hundreds to thousands of person-hours. We previously introduced a Scenario Planning Advisor prototype (Sohrabi et al. 2018a,b) that focuses on generating scenarios quickly based on expert-developed models. We present the evolution of that prototype into a full-scale, cloud deployed ERM solution that: (i) can automatically (through NLP) create models from authoritative documents such as books, reports and articles, such that what typically took hundreds to thousands of person-hours can now be achieved in minutes to hours; (ii) can gather news and other feeds relevant to forces in the risk models and group them into storylines without any other user input; (iii) can generate scenarios at scale, starting with dozens of forces of interest from models with thousands of forces in seconds; (iv) provides interactive visualizations of scenario and force model graphs, including a full model editor in the browser. The SPA solution is deployed under a non-commercial use license at https://spa-service.draco.res.ibm.com and includes a user guide to help new users get started. A video demonstration is available at https://www.youtube.com/watch?v=IaX3d37NUl8.

The need for finding a set of plans rather than one has been motivated by a variety of planning applications. The problem is studied in the context of both diverse and top-k planning: while diverse planning focuses on the difference between pairs of plans, the focus of top-k planning is on the quality of each individual plan. Recent work in diverse planning introduced additionally restrictions on solution quality. Naturally, there are application domains where diversity plays the major role and domains where quality is the predominant feature. In both cases, however, the amount of produced plans is often an artificial constraint, and therefore the actual number has little meaning. Inspired by the recent work in diverse planning, we propose a new family of computational problems called top-quality planning, where solution validity is defined through plan quality bound rather than an arbitrary number of plans. Switching to bounding plan quality allows us to implicitly represent sets of plans. In particular, it makes it possible to represent sets of plans that correspond to valid plan reorderings with a single plan. We formally define the unordered top-quality planning computational problem and present the first planner for that problem. We empirically demonstrate the superior performance of our approach compared to a top-k planner-based baseline, ranging from 41% increase in coverage for finding all optimal plans to 69% increase in coverage for finding all plans of quality up to 120% of optimal plan cost. Finally, complementing the new approach by a complete procedure for generating all valid reorderings of a given plan, we derive a top-quality planner. We show the planner to be competitive with a top-k planner based baseline.

Scenario planning is a commonly used method by companies to develop their long-term plans. Scenario planning for risk management puts an added emphasis on identifying and managing emerging risk. While a variety of methods have been proposed for this purpose, we show that applying AI planning techniques to devise possible scenarios provides a unique advantage for scenario planning. Our system, the Scenario Planning Advisor (SPA), takes as input the relevant information from news and social media, representing key risk drivers, as well as the domain knowledge and generates scenarios that explain the key risk drivers and describe the alternative futures. To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to planning. Furthermore, we describe the computation of the scenarios, lessons learned, and the feedback received from the pilot deployment of the SPA system in IBM.

We present the IBM Research Scenario Planning Advisor (SPA), a decision support system that allows users to generate diverse alternate scenarios of the future and enhance their ability to imagine the different possible outcomes, including unlikely but potentially impactful futures. The system includes tooling for experts to intuitively encode their domain knowledge, and uses AI Planning to reason about this knowledge and the current state of the world, including news and social media, when generating scenarios.

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.

In this paper, we address the problem of finding diverse high-quality plans motivated by the hypothesis generation problem. To this end, we present a planner called TK*that first efficiently solves the “top-k” cost-optimal planning problem to find k best plans, followed by clustering to produce diverse plans as cluster representatives.

In this paper we apply AI planning to address the hypothesis exploration problem and provide assistance to network administrators in detecting malware based on unreliable observations derived from network traffic.Building on the already established characterization and use of AI planning for similar problems, we propose a formulation of the hypothesis generation problem for malware detection as an AI planning problem with temporally extended goals and actions costs. Furthermore, we propose a notion of hypothesis ``plausibility'' under unreliable observations, which we model as plan quality. We then show that in the presence of unreliable observations, simply finding one most ``plausible'' hypothesis, although challenging, is not sufficient for effective malware detection. To that end, we propose a method for applying a state-of-the-art planner within a principled exploration process, to generate multiple distinct high-quality plans. We experimentally evaluate this approach by generating random problems of varying hardness both with respect to the number of observations, as well as the degree of unreliability. Based on these experiments, we argue that our approach presents a significant improvement over prior work that are focused on finding a single optimal plan, and that our hypothesis exploration application can motivate the development of new planners capable of generating the top high-quality plans.

Efficient storage and querying of RDF data is of increasing importance, due to the increased popularity and widespread acceptance of RDF on the web and in the enterprise. In this paper, we describe a novel storage and query mechanism for RDF which works on top of existing relational representations. Reliance on relational representations of RDF means that one can take advantage of 35+ years of research on efficient storage and querying, industrial-strength transaction support, locking, security, etc. However, there are significant challenges in storing RDF in relational, which include data sparsity and schema variability. We describe novel mechanisms to shred RDF into relational, and novel query translation techniques to maximize the advantages of this shredded representation. We show that these mechanisms result in consistently good performance across multiple RDF benchmarks, even when compared with current state-of-the-art stores. This work provides the basis for RDF support in DB2 v.10.1.

The widespread adoption of the Resource Description Framework (RDF) for the representation of both open web and enterprise data is the driving force behind the increasing research interest in RDF data management. As RDF data management systems proliferate, so are benchmarks to test the scalability and performance of these systems under data and workloads with various characteristics.

There is now a growing need to identify various kinds of activities that occur in videos. In this paper, we first present a logical language called Probabilistic Activity Description Language (PADL) in which users can specify activities of interest. We then develop a probabilistic framework which assigns to any subvideo of a given video sequence a probability that the subvideo contains the given activity, and we finally develop two fast algorithms to detect activities within this framework. OffPad finds all minimal segments of a video that contain a given activity with a probability exceeding a given threshold. In contrast, the OnPad algorithm examines a video during playout (rather than afterwards as OffPad does) and computes the probability that a given activity is occurring (even if the activity is only partially complete). Our prototype Probabilistic Activity Detection System (PADS) implements the framework and the two algorithms, building on top of existing image processing algorithms. We have conducted detailed experiments and compared our approach to four different approaches presented in the literature. We show that-for complex activity definitions-our approach outperforms all the other approaches.

Real-world use of RDF requires the ability to transparently represent and explain metadata associated with RDF triples. For example, when RDF triples are extracted automatically by information extraction programs, there is a need to represent where the triples came from, what their temporal validity is, and how certain we are that the triple is correct. Today, there is no theoretically clean and practically scalable mechanism that spans these different needs - reification is the only solution propose to date, and its implementations have been ugly. In this paper, we present Annotated RDF (or aRDF for short) in which RDF triples are annotated by members of a partially ordered set (with bottom element) that can be selected in any way desired by the user. We present a formal declarative semantics (model theory) for annotated RDF and develop algorithms to check consistency of aRDF theories and to answer queries to aRDF theories. We show that annotated RDF supports users who need to think about the uncertainty, temporal aspects, and provenance of the RDF triples in an RDF database. We develop a prototype aRDF implementation and show that our algorithms work efficiently even on real world data sets containing over 10 million triples.

In this paper, we tackle the problem of helping domain experts to construct, parameterize and deploy mashups of data and code. We view a mashup as a data processing flow, that describes how data is obtained from one or more sources, processed by one or more components, and finally sent to one or more sinks. Our approach allows specifying patterns of flows, in a language called Cascade. The patterns cover different possible variations of the flows, including variations in the structure of the flow, the components in the flow and the possible parameterizations of these components. We present a tool that makes use of this knowledge of flow patterns and associated metadata to allow domain experts to explore the space of possible flows described in the pattern. The tool uses an AI planning approach to automatically build a flow, belonging to the flow pattern, from a high-level goal, specified as a set of tags. We describe examples from the financial services domain to show the use of flow patterns in allowing domain experts to construct a large variety of mashups rapidly.

The World Wide Web Consortium's RDF standard primarily consists of (subject, property, object) triples that specify the value that a given subject has for a given property. However, it is frequently the case that even for a fixed subject and property, the value varies with time. As a consequence, efforts have been made to annotate RDF triples with "valid time" intervals. However, to date, no proposals exist for efficient indexing of such temporal RDF databases. It is clearly beneficial to store RDF data in a relational DB - however, standard relational indexes are inadequately equipped to handle RDF's graph structure. In this paper, we propose the tGRIN index structure that builds a specialized index for temporal RDF that is physically stored in an RDBMS. Past efforts to store RDF in relational stores include Jena2 from HP, Sesame from OpenRDF.org, and 3store from the University of Southampton. We show that even when these efforts are augmented with well known temporal indexes like R+ trees, SR-trees, ST-index, and MAP21, the tGRIN index exhibits superior performance. In terms of index build time, tGRIN takes two thirds or less of the time used by any other system, and it uses a comparable amount of memory and less disk space than Jena, Sesame and 3store. More importantly, tGRIN can answer queries three to six times faster for average query graph patterns and five to ten times faster for complex queries than these systems.

The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.

Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension-Probabilistic Petri Nets (PPN)-and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.

Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension—Probabilistic Petri Nets (PPN)—and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.

There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies. We evaluate our algorithm on a set of 30 pairs of OWL Lite ontologies with the schema and data matchings found by human reviewers. We compare against two systems-the ontology matching tool FCA-merge [28] and the schema matching tool COMA++ [1]. ILIADS shows an average improvement of 25 % in quality over FCA-merge and a 11% improvement in recall over COMA++.