Experts report on the latest artificial intelligence research concerning reasoning about reasoning itself. "This text provides a clear and systematic development of the essentials of mobile robotics. The second edition adds up-to-date material to a book that has already been adopted in robotics classes worldwide. With this guide in hand, students and readers will swiftly navigate the field toward more advanced systems." The author's Selective Tuning model of visual attention provides a framework that integrates the various expressions of visual attention and the biology of the visual system grounded in the logic of computation."

The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The American Association for Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning. Spatial and Temporal Reasoning Technical reports containing the papers of these workshops are available from AAAI Press.

For example, an ASP program encoding a planning scenario has as many models as valid plans. This schema is similar to that underlying the application of propositional satisfiability (SAT) algorithms. In fact, the ranges of applicability of these two techniques are similar. The American Association for Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2001 Spring Symposium Series on Monday through Wednesday, 26 to 28 March 2001, at Stanford University. The symposium began with Bart Selman's invited lecture on randomized methods for SAT.

It was a pleasure to participate in the symposium entitled Building Dialogue Systems for Tutorial Applications along with 44 researchers from a variety of relevant application areas. The American Association for Artificial Intelligence presented the 2000 Fall Symposium Series was held on Friday through Sunday, 3 to 5 November, at the Sea Crest Oceanfront Conference Center. "Is'hintify' an acceptable morphological construction in standard English?" Nine working systems were showcased in two lively demo sessions. Knowing how to do things is an important category of knowledge underlying many kinds of intelligent behavior in artificial agents, such as critiquing, advice giving, tutoring, collaboration, and delegation.

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We argue that developing DCP systems will be necessary for planning applications to be successful in these environments.

The main contribution of the book Rules of Encounter: Designing Conventions for Automated Negotiation, by Jeffrey S. Rosenschein and Gilad Zlotkin, is the formulation of a principled framework within which to study interactions among artificial heterogeneous agents. This framework is based on the theory of games, which is aimed at decision problems faced by agents in situations in which the agent's welfare depends not only on its own actions but also on the actions of other agents. The examples are numerous: The personal digital assistants (PDAs) that might one day keep track of their users' itinerary will have to negotiate with PDAs of other people to adjust and synchronize their meeting schedules. Software agents looking for the right kinds of information on the Internet on behalf of their users might have to negotiate with other such agents over the access to resources. Computer agents that control a telecommunications network will have to interact with computers that control other networks and might find it beneficial to come to agreement with them.

This article contains the reports of the AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents symposia, which were held November 12-14, 2015 in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from five of the symposia. Human-robot interaction (HRI) is a broad community encompassing robotics, artificial intelligence (AI), human-computer interaction (HCI), psychology, and social science. In this meeting, we sought to bring together and strengthen the subset of the HRI community that is focused on the AI challenges inherent to HRI.

You watch the feed from the onboard camera as the rover rolls along the surface, when you notice the terrain changing ahead, so you instruct the rover to turn. The problem? You're 6 minutes too late. Due to the speed-of-light delay in communication between yourself and the rover, your monolithic multimillion dollar project is in pieces at the bottom of a Martian canyon, and the nearest repairman is 65 million miles away. There are, of course, solutions to this type of problem. You can instruct it to travel a very small distance and reevaluate the rover's situation before the next round of travel, but this leads to painfully slow processes that take orders of magnitude longer than they would on Earth.

Each session consisted of two papers, each of which was allotted 25 minutes for presentation, and a 25-minute discussion that was led by a member of the organizing committee. This format was adopted because the organizers believed that the single day that was allocated to the workshop was conducive to in-depth presentation of a small number of papers that would lead to issue-oriented discussions. The sections in this article bring forward points that were made during the workshop's four sessions. Recent attempts to develop larger and more complex knowledge-based systems have revealed the shortcomings and problems of centralized, single-agent architectures and have acted as a springboard for research in distributed AI (DAI). Although initial research efforts in DAI concentrated on issues relating to homogeneous systems (that is, systems using agents of a similar type or with similar knowledge), there is now increasing interest in systems comprised of heterogeneous components.

This article describes the winning entries in the 2000 American Association for Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication. As any AI researcher knows, developing one robot for a real-world task such as serving hors d'oeuvres to a crowded room can be a significant undertaking. Our plan was to have a team of three agents participate in the hors d'oeuvres-serving contest and have one mobile robot attempt the Urban-Search-and-Rescue (USAR) event.