Intentions, an integral part of the mental state of an agent, play an important role in

An architecture for a rational agent must allow for means-end reasoning, for the weighing of competing alternatives, and for interactions between these two forms of reasoning. Such an architecture must also address the problem of resource boundedness. We sketch a solution of the first problem that points the way to a solution of the second. In particular, we present a high-level specification of the practical-reasoning component of an architecture for a resource-bounded rational agent. In this architecture, a major role of the agent's plans is to constrain the amount of further practical reasoning she must perform. Bratman, Israel, and Pollack 3 1 Introduction Rational behavior---the production of actions that further the goals of an agent, based upon her conception of the world---has long interested researchers in artificial intelligence, who are attempting to build machines that behave rationally, as well as philosophers of mind and action, decision theorists, and others who are a...

We are investigating techniques for developing distributed and adaptive collections of agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate a user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for constructing agents. We call this reusable multi-agent computational infrastructure RETSINA (Reusable Task Structure-based Intelligent Network Agents). It has three types of agents. Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide syste...

The development of reasoning systems that can reason and plan in a continuously changing environment is emerging as an important area of research in Artificial Intelligence. This paper describes some of the features of a Procedural Reasoning System (PRS) that enables it to operate e ectively in such environments. The basic system design is first described and it is shown how this architecture supports both goal-directed reasoning and the ability toreact rapidly to unanticipated changes in the environment. The decision-making capabilities of the system are then discussed and it is indicated how the system integrates these components in a manner that takes account of the bounds on both resources and knowledge that typify most real-time operations. The system has been applied to handling malfunctions on the space shuttle, threat assessment, and the control of an autonomous robot.

We investigate the semantics for Knowledge Query Manipulation Language (KQML) and we propose a semantic framework for the language. KQML is a language and a protocol to support communication between software agents. Based on ideas from speech act theory,we propose a semantic description for KQML that associates descriptions of the cognitive states of agents with the use of the language 's primitives (performatives). We use this approachto describe the semantics for the basic set of KQML performatives. We also investigate implementation issues related to our semantic approach. We suggest that KQML can o#er an all purpose communication language for software agents that requires no limiting pre-commitments on the agents' structure and implementation. KQML can provide the Distributed AI, Cooperative Distributed Problem Solving and Software Agents communities with an all purpose language and environment for intelligent inter-agent communication.

Recent research in real-time Artificial Intelligence has focussed upon the design of situated agents and, in particular, how to achieve effective and robust behaviour with limited computational resources. A range of architectures and design principles has been proposed to solve this problem. This has led to the development of simulated worlds that can serve as testbeds in which the effectiveness of different agents can be evaluated. We report here an experimental program that aimed to investigate how commitment to goals contributes to effective behaviour and to compare the properties of different strategies for reacting to change. Our results demonstrate the feasibility of developing systems for empirical measurement of agent performance that are stable, sensitive, and capable of revealing the effect of "high-level" agent characteristics such as commitment. 1

. Adaptation in open, multi-agent information gathering systems is important for several reasons. These reasons include the inability to accurately predict future problem-solving workloads, future changes in existing information requests, future failures and additions of agents and data supply resources, and other future task environment characteristic changes that require system reorganization. We have developed a multi-agent distributed system infrastructure, Retsina (REusable Task Structure-based Intelligent Network Agents) that handles adaptation in an open Internet environment. Adaptation occurs both at the individual agent level as well as at the overall agent organization level. The Retsina system has three types of agents. Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating p...

ion-Partitioned Evaluator (APE) architecture which has been tested in a simulated, single-agent, indoor navigation domain [SH90]. The APE architecture is composed of a number of concurrent, hierarchically abstract action control layers, each representing and reasoning about some particular aspect of the agent's task domain. Implemented as a parallel blackboard-based planner, the five layers --- sensor/motor, spatial, temporal, causal, and conventional (general knowledge) --- effectively partition the agent's data processing duties along a number of dimensions including temporal granularity, information/resource use, and functional abstraction. Perceptual information flows strictly from the agent sensors (connected to the sensor /motor level) toward the higher levels, while command or goal-achievement information flows strictly downward towards the agent's effectors (also connected to the sensor/motor level). Besides mechanisms for communicating with other layers, each layer in the AP...

We present a formalization of the Recursive Modeling Method, which we have previously, somewhat informally, proposed as a method that autonomous artificial agents can use for intelligent coordination and communication with other agents. Our formalism is closely related to models proposed in the area of game theory, but contains new elements that lead to a different solution concept. The advantage of our solution method is that always yields the optimal solution, which is the rational action of the agent in a multi-agent environment, given the agent's state of knowledge and its preferences, and that it works in realistic cases when agents have only a finite amount of information about the agents they interact with. Introduction Since its initial conceptual development several years ago (Gmytrasiewicz, Durfee, & Wehe 1991a; 1991b), the Recursive Modeling Method (RMM) has provided a powerful decision-theoretic underpinning for coordination and communication decisionmaking, including dec...

this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system architecture has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents