By perceiving the with no adverse effects on the achievement world, each fully distributed agent builds a of G. Then, based can be thought of as times at which the on a complex set of behaviors, it chooses an team is "offline." In general (that is, when the agents are Although acting autonomously, each agent "online"), the domain is dynamic and real time, contributes to the overall team's goal. Agents receive sensory p at time t. The world state is directly accessible In the extreme, if q 0 or if x 0, then the to both internal and external behaviors.

Although our previous and processes the images, giving the positions team had accurate navigation, it was not easily of each robot and the ball. This information is interruptible, which is necessary for operating sent to an off-board controller and distributed in a highly dynamic environment. The final design includes a battery of inherent mechanical inaccuracies and module supplying three independent unforeseen interventions from other agents. It also includes a single board RoboCup competition in Paris (Stone, Veloso, containing all the required electronic circuitry and Riley 1999; Kitano et al. 1997). These improvements by an array of four infrared sensors, which include a robust low-level control algorithm, which handles a moving target with is enabled or disabled by the software control.

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. The program included 16 workshops covering a wide range of topics in AI. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications.

There's More to Life Than Making Plans: Plan Management in Dynamic, Multiagent Environments

In this article, I describe several challenges facing the integration of two distinct lines of AI research: (1) decision-theoretic planning (DTP) and (2) multiagent systems. Both areas (especially the second) are attracting considerable interest, but work in multiagent systems often assumes either classical planning models or prespecified economic valuations on the part of the agents in question. By integrating models of DTP in multiagent systems research, more sophisticated multiagent planning scenarios can be accommodated, at the same time explaining precisely how agents determine their valuations for different sources or activities. I also briefly mention some opportunities afforded planning agents in multiagent settings and how these might be addressed.

People often act together with a shared purpose; they collaborate. Collaboration enables them to work more efficiently and to complete activities they could not accomplish individually. An increasing number of computer applications also require collaboration among various systems and people. Thus, a major challenge for AI researchers is to determine how to construct computer systems that are able to act effectively as partners in collaborative activity. Collaborative activity entails participants forming commitments to achieve the goals of the group activity and requires group decision making and group planning procedures. In addition, agents must be committed to supporting the activities of their fellow participants in support of the group activity. Furthermore, when conflicts arise (for example, from resource bounds), participants must weigh their commitments to various group activities against those for individual activities. This article briefly reviews the major features of one model of collaborative planning called SHARED-PLANS (Grosz and Kraus 1999, 1996). It describes several current efforts to develop collaborative planning agents and systems for human-computer communication based on this model. Finally, it discusses empirical research aimed at determining effective commitment strategies in the SHAREDPLANS context.

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. We give a historical overview of research leading to the current state of the art in DCP and describe research in distributed and continual planning.

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In this article, I describe several challenges facing the integration of two distinct lines of AI research: (1) decision-theoretic planning (DTP) and (2) multiagent systems. Both areas (especially the second) are attracting considerable interest, but work in multiagent systems often assumes either classical planning models or prespecified economic valuations on the part of the agents in question. By integrating models of DTP in multiagent systems research, more sophisticated multiagent planning scenarios can be accommodated, at the same time explaining precisely how agents determine their valuations for different sources or activities. I discuss several research challenges that emerge from this integration, involving the development of coordination protocols, the reasoning about lack of coordination, and the predicting of behavior in markets. I also briefly mention some opportunities afforded planning agents in multiagent settings and how these might be addressed.