Goals, as typically conceived in AI planning, provide an insufficient basis for choice of action, and hence are deficient as the sole expression of an agent's objectives. Decision-theoretic utilities offer a more adequate basis, yet lack many of the computational advantages of goals. We provide a preferential semantics for goals that grounds them in decision theory and preserves the validity of some, but not all, common goal operations performed in planning. This semantic account provides a criterion for verifying the design of goal-based planning strategies, thus providing a new framework for knowledge-level analysis of planning systems. Planning to achieve goals In the predominant AI planning paradigm, planners construct plans designed to produce states satisfying particular conditions called goals. Each goal represents a partition of possible states of the world into those satisfying and those not satisfying the goal. Though planners use goals to guide their reasoning, the crude b...

In this paper, we use an extended version of the situation calculus to formalize goals and rational action. We then use these notions and a definition of ability (Lesp'erance et al. 1995b) to show that an agent that is acting rationally will achieve its goals when it is able to do so. 1 Introduction This paper describes work on rational action that arose from our efforts to create an explicit representation of the goals of agents in the situation calculus. The utility of an explicit representation of the goals of agents is evident when we consider domains with multiple interacting agents. In domains where agents are communicating and cooperating to perform a task, the ability to specify the knowledge and goals of the agents becomes useful in order to determine that the agents can perform their parts of the task and have the required commitment to see their parts to completion. Designers of agents can use this information to help predict the behaviors of the agents they create. The ag...

The notion of commitment is central to understanding agents and multiagent systems. At least two kinds of commitment can be identified in the AI literature---the internal or psychological and the external or social. While these notions must not be conflated with each other, they are not entirely unrelated. We review the historical development of commitments in AI and distributed computing, and show the various roles they might play in multiagent systems. We discuss the key interrelationships among these concepts, and study their implementational aspects, both in traditional individual agents, and in agents that are recursively created as systems of other agents. We close with a discussion of the key research challenges. This is a considerably revised version of a DFKI Technical Memo. Parts of it were presented at a panel discussion at the 1991 AAAI Fall Symposium on Knowledge and Action at the Social and Organizational Levels. An invited presentation in a panel on Agents and Commitme...

In this paper, we describe a framework for specifying communicative multi-agent systems, using a theory of action based in the situation calculus to describe the effects of actions on the world and on the mental states of agents; and the concurrent, logic programming language ConGolog to specify the actions performed by each agent. Since ConGolog has a well-defined semantics in the situation calculus, the specifications can be used to reason about the behavior of individual agents and the system as a whole. We extend the work presented in (Lesp'erance et al. 1996) to allow the specifications to mention agents' goals explicitly. The framework presented here allows the behavior of different agents to be specified at different levels of abstraction, using a rich set of programming language constructs. As an example, we specify a meeting scheduler multiagent system. 1 Introduction Many agent theories (some examples are (Cohen & Levesque 1990a; 1990b; Rao & Georgeff 1991; Singh 1994)) fol...

This paper summarises our on-going work on mixed-initiative decision making which extends both classical decision theory and a symbolic theory of decision making based on argumentation to a multi-agent domain.

. Rationality is a useful metaphor for understanding autonomous, intelligent agents. A persuasive view of intelligent agents uses cognitive primitives such as intentions and beliefs to describe, explain, and specify their behavior. These primitives are often associated with a notion of commitment that is internal to the given agent. However, at first sight, there is a tension between commitments and rationality. We show how the two concepts can be reconciled for the important and interesting case of limited, intelligent agents. We show how our approach extends to handle more subtle issues such as precommitments, which have previously been assumed to be conceptually too complex. We close with a proposal to develop conative policies as a means to represent commitments in a generic, declarative manner. 1 Introduction How can limited agents cope with a complex world? This is a question that has drawn much attention in the study of intelligent agents. As agents find application in an incre...

Although much progress has been made in agent theory and practice, bottlenecks remain in the construction of complex multiagent systems. We introduce interaction-oriented programming (IOP) as an approach to orchestrate the interactions among agents. IOP is more tractable and practical than general agent programming, especially in settings such as open information environments, where the internal details of autonomously developed agents are not available. IOP facilitates multiagent system design by enabling declarative specification and enactment of agent interactions, thereby channeling the intellectual energies of designers into the most amenable and effective design tasks. We develop an event algebra to specify interactions among agents. We automatically compile these declarative specifications into executable temporal logic constraints. These are efficiently processed at run-time to produce the desired behavior in a distributed manner. We have implemented the above modules in a (co...

In this article we will describe our research efforts in coping with a trade-off that can be often found in the control and optimization of todays business processes. Though centralized control may achieve nearto -optimum results in optimizing the system behavior, there are usually social, technical and security restrictions on applying centralized control. Distributed control on the other hand may cope with these restrictions but also entails sub-optimality and communicational overhead. Our concept of composable agents tries to allow a dynamic and fluent transition between globalization and localization in business process control by adapting to the current real-world system structure. We are currently evaluating this concept in the framework of patient flow control at Charit'e Berlin. Introduction Research in Distributed Artificial Intelligence (DAI, (Bond & Gasser 1988)) has been traditionally divided into Distributed Problem Solving (DPS) and Multi Agent Systems (MAS). However, r...

Rationality is an important concept in Artificial Intelligence and Philosophy.

In this article we describe our research efforts in coping with a trade-off that can be often found in the control and optimization of todays business processes. Though centralized control may achieve better results in controlling the system behavior, there are usually social, technical and security constraints on applying centralized control. Distributed control on the other hand may cope with these constraints but also entails suboptimal results and communicational overhead. Our concept of composable agents tries to allow a dynamic and fluent transition between globalization and localization in business process control by adapting to the current real-world system structure. We are currently evaluating this concept in the framework of a patient flow control project at Charit'e Berlin. Todays applications of information technology face at least two major aspects of business settings. The first aspect is the partially or fully automated execution of complex business processes. This enfo...