In real world, the regions occupied by these entities may the location of an object, involve dealing with spatial properties have holes (e.g., Store A may have a small garden in the and relations of objects. For higher precision of solutions, middle) or may be disconnected (e.g., Store A may consist if data is available, quantitative approaches can be of two parts across a small street). Moreover, the given set of employed to find metric solutions for these tasks. On the constraints may be incomplete (i.e., qualitative spatial relations other hand, for some applications (e.g., exploration of an between some spatial objects are not known) or some unknown environment), quantitative data may not always be constraints may involve disjunctions (e.g., missing child is available due to incomplete knowledge about the environment; to the south of Store A or to the north of Store B). In such and, for some applications (e.g., that involve humanrobot cases, with uncertainty or incomplete knowledge, checking interactions) sociable and understandable interactions the consistency of a given set of constraints is NPcomplete and acceptable explanations are often more desirable than (Table 1).

ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm.

The answer sets for the given program can then be computed by special software systems called answer set solvers, such as DLV, Smodels, or clasp. It is especially relevant to language processing and understanding, learning, reasoning with so called defaults -- statements of the theory update/revision, preferences, diagnosis, form "Normally (typically, as a rule) elements of class description logics, semantic web, multicontext systems, C have property P." We all learn rather early in life and argumentation. Other areas that include that parents normally love their children, citizens are applications of ASP are, for instance, computational normally required to pay taxes, and so forth. We also biology, systems biology, bioinformatics, automatic learn, however, that these rules are not absolute and music composition, assisted living, software engineering, allow various types of exceptions. It is natural to bounded model checking, and robotics. Learning correct ways to decision support systems (Nogueira et al. 2001) (used reason with defaults and their exceptions is necessary by United Space Alliance), automated product configuration for building an agent capable of using such a KB. One (Tiihonen, Soininen, and Sulonen 2003) of the best available solutions to this problem uses (used by Variantum Oy), intelligent call routing the knowledge representation language CR-Prolog (Leone and Ricca 2015) (used by Italia Telecom) and (Balduccini and Gelfond 2003) -- a simple extension configuration and reconfiguration of railway safety of the original ASP language of logic programs with systems (Aschinger et al. 2011) (used by Siemens).

We consider multiple teams of heterogeneous robots, where each team is given a feasible task to complete in its workspace on its own, and where teams are allowed to transfer robots between each other. We study the problem of finding a coordination of robot transfers between teams to ensure an optimal global plan (with minimum makespan) so that all tasks can be completed as soon as possible by helping each other. We propose to solve this problem using answer set programming.

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.

We propose the use of causality-based formal representation and automated reasoning methods to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as, optimal planning and diagnostic reasoning. We introduce algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We discuss how these algorithms can be embedded in an execution and monitoring framework, and show their applicability on an intelligent painting factory scenario.

We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level reasoning to address hybrid planning problems in robotics. We identify four distinct strategies for such an integration: (i) low-level checks are done for all possible cases in advance and then this information is used during plan generation, (ii) low-level checks are done exactly when they are needed during the search for a plan, (iii) first all plans are computed and then infeasible ones are filtered, and (iv) by means of replanning, after finding a plan, low-level checks identify whether it is infeasible or not; if it is infeasible, a new plan is computed considering the results of previous low-level checks. We perform experiments on hybrid planning problems in robotic manipulation and legged locomotion domains considering these four methods of integration, as well as some of their combinations. We analyze the usefulness of levels of integration in these domains, both from the point of view of computational efficiency (in time and space) and from the point of view of plan quality relative to its feasibility. We discuss advantages and disadvantages of each strategy in the light of experimental results and provide some guidelines on choosing proper strategies for a given domain.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2011 Spring Symposium Series Monday through Wednesday, March 21–23, 2011 at Stanford University. The titles of the eight symposia were AI and Health Communication, Artificial Intelligence and Sustainable Design, AI for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes.

The titles of the eight symposia were Artificial Intelligence and Health Communication, Artificial Intelligence and Sustainable Design, Artificial Intelligence for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes. The goal of the Artificial Intelligence and Health Communication symposium was to advance the conceptual design of automated systems that provide health services to patients and consumers through interdisciplinary insight from artificial intelligence, health communication and related areas of communication studies, discourse studies, public health, and psychology. There is a large and growing interest in the development of automated systems to provide health services to patients and consumers. In the last two decades, applications informed by research in health communication have been developed, for example, for promoting healthy behavior and for managing chronic diseases. While the value that these types of applications can offer to the community in terms of cost, access, and convenience is clear, there are still major challenges facing design of effective health communication systems. Overall, the participants found the format of the symposium engaging and constructive, and they The symposium was organized around five main expressed the desire to continue this initiative in concepts: (1) Patient empowerment and education further events.

Some of these complex queries, such as Q1 or Q2, Recent advances in health and life sciences have led to generation can be represented in a formal query language (e.g., of a large amount of biomedical data. To facilitate access SQL/SPARQL) and then answered using Semantic Web to its desired parts, such a big mass of data has been represented technologies. However, queries, like Q4, that require auxiliary in structured forms, like biomedical ontologies and recursive definitions (such as transitive closure) cannot databases. On the other hand, representing these biomedical be directly represented in these languages; and thus such ontologies and databases in different forms, constructing queries cannot be answered directly using Semantic Web them independently from each other, and storing them at technologies. The experts usually compute auxiliary relations different locations have brought about many challenges for externally, for instance, by enumerating all drug-drug answering queries about the knowledge represented in these interaction chains or gene cliques, and then use these auxiliary ontologies and databases.