The study of computational agents capable of rational behaviour has received a great deal of attention in recent years. Theoretical formalizations of such agents and their implementations have proceeded in parallel with little or no connection between them. This paper explores a particular type of rational agent, a BeliefDesire -Intention (BDI) agent. The primary aim of this paper is to integrate (a) the theoretical foundations of BDI agents from both a quantitative decision-theoretic perspective and a symbolic reasoning perspective; (b) the implementations of BDI agents from an ideal theoretical perspective and a more practical perspective; and (c) the building of large-scale applications based on BDI agents. In particular, an air-traffic management application will be described from both a theoretical and an implementation perspective.

This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.

The need for negotiation in multi-agent systems stems from the requirement for agents to solve the problems posed by their interdependence upon one another. Negotiation provides a solution to these problems by giving the agents the means to resolve their conflicting objectives, correct inconsistencies in their knowledge of other agents' world views, and coordinate a joint approach to domain tasks which benefits all the agents concerned. We propose a framework, based upon a system of argumentation, which permits agents to negotiate in order to establish acceptable ways of solving problems. The framework provides a formal model of argumentation-based reasoning and negotiation, details a design philosophy which ensures a clear link between the formal model and its practical instantiation, and describes a case study of this relationship for a particular class of architectures (namely those for belief-desire-intention agents). 1 Introduction An increasing number of software app...

JAM is a hybrid intelligent agent architecture that draws upon the theories and ideas of the Procedural Reasoning System (PRS), Structured Circuit Semantics (SCS), and Act plan interlhtgua. Furthermore, JAM draws upon the implementation pragmatics of the University of Michigan’s and SRI Internatlonal’s implementation of PRS (UMPRS and PRS-CL, respectively). JAM provides rich and extensive plan and procedural representations, metalevel and utility-based reasoning over multiple simultaneous goals, and goal-driven and event-driven behavior that are an amalgam of all of the sources listed above. The JAM agent architecture also provides an agentGo primitive function utilizing Java’s object serialization mechanism to provide widely-supported mobility capabilities.

Design-to-time is an approach to problem-solving in resource-constrained domains where: multiple solution methods are available for tasks, those solution methods make tradeoffs in solution quality versus time, and satisficing solutions are acceptable. Design-to-time involves designing a solution to a problem that uses all available resources to maximize the solution quality within the available time. This paper defines the design-to-time approach in detail, contrasting it to the anytime algorithm approach, and presents a heuristic algorithm for designto -time real-time scheduling. Our blackboard architecture that implements the design-to-time approach is discussed and an example problem and solution from the Distributed Vehicle Monitoring Testbed (DVMT) is described in detail. Experimental results, generated using a simulation, show the effects of various parameters on scheduler performance. Finally we discuss future research goals and plans. 1 This work was partly supported by the Of...

Agents situated in dynamic environments benefit from having a repertoire of plans, supplied in advance, that permit them to rapidly generate appropriate sequences of actions in response to important events. When agents can form teams, new problems emerge regarding the representation and execution of joint actions. In this paper we introduce a language for representing joint plans for teams of agents, we describe how agents can organize the formation of a suitably skilled team to achieve a joint goal, and we explain how such a team can execute these plans to generate complex, synchronized team activity. The formalism provides a framework for representing and reasoning about joint actions in which various approaches to co-ordination and commitment can be explored. 1 Introduction A rational agent can be viewed as a system continuously receiving perceptual input from the environment in which it is embedded and responding by taking actions that affect that environment. It can be characte...

Like classical planning, the execution of high-level agent programs requires a reasoner to look all the way to a final goal state before even a single action can be taken in the world. This deferral is a serious problem in practice for large programs. Furthermore, the problem is compounded in the presence of sensing actions which provide necessary information, but only after they are executed in the world. To deal with this, we propose (characterize formally in the situation calculus, and implement in Prolog) a new incremental way of interpreting such high-level programs and a new high-level language construct, which together, and without loss of generality, allow much more control to be exercised over when actions can be executed. We argue that such a scheme is the only practical way to deal with large agent programs containing both nondeterminism and sensing.

An important concept for intelligent agent systems is goals. Goals have two aspects: declarative (a description of the state sought), and procedural (a set of plans for achieving the goal). A declarative view of goals is necessary in order to reason about important properties of goals, while a procedural view of goals is necessary to ensure that goals can be achieved efficiently in dynamic environments. In this paper we propose a framework for goals which integrates both views. We discuss the requisite properties of goals and the link between the declarative and procedural aspects, then derive a formal semantics which has these properties. We present a high-level plan notation with goals and give its formal semantics.

The study of computational agents capable of rational behaviour has received a great deal of attention in recent years. A number of theoretical formalizations for such multiagent systems have been proposed. However, most of these formalizations do not have a strong semantic basis nor a sound and complete axiomatization. Hence, it has not been clear as to how these formalizations could be used in building agents in practice. This paper explores a particular type of multi-agent system, in which each agent is viewed as having the three mental attitudes of belief (B), desire (D), and intention (I). It provides a family of multi-modal branching-time BDI logics with a semantics that is grounded in traditional decision theory and a possible-worlds framework, categorizes them, provides sound and complete axiomatizations, and gives constructive tableaubased decision procedures for testing the satisfiability and validity of formulas. The computational complexity of these decision procedures is n...

The need for negotiation in multi-agent systems stems from the requirement for agents to solve the problems posed by their interdependence upon one another. Negotiation provides a solution to these problems by giving the agents the means to resolve their conflicting objectives, correct inconsistencies in their knowledge of other agents' world view, and coordinate a joint approach to domain tasks which benefits all the agents concerned. We propose a framework, based upon a system of argumentation, which permits agents to negotiate to establish acceptable ways to solve problems.