The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents; researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the prop-erties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

The concept of an agent has recently become important in Artificial Intelligence (AI), and its relatively youthful subfield, Distributed AI (DAI). Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide the area into three themes (though as the reader will see, these divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents; researchers in this area are primarily concerned with the problem of constructing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages typically embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the key issues, and point to work that elaborates on them. The paper closes with a detailed bibliography, and some bibliographical remarks. 1

MultiLanguage systems (ML systems) are formal systems allowing the use of multiple distinct logical languages. In this paper we introduce a class of ML systems which use a hierarchy of first order languages, each language containing names for the language below, and propose them as an alternative to modal logics. The motivations of our proposal are technical, epistemological and implementational. From a technical point of view, we prove, among other things, that the set of theorems of the most common modal logics can be embedded (under the obvious bijective mapping between a modal and a first order language) into that of the corresponding ML systems. Moreover, we show that ML systems have properties not holding for modal logics and argue that these properties are justified by our intuitions. This claim is motivated by the study of how ML systems can be used in the representation of beliefs (more generally, propositional attitudes) and provability, two areas where modal logics have been extensively used. Finally, from an implementation point of view, we argue that ML systems resemble closely the current practice in the computer representation of propositional attitudes and metatheoretic theorem proving.

The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. 1 Introduction People make judgments of rationality all the time, usually in criticizing someone else's thoughts or deeds as irrational, or in defending their own as rational. Artificial intelligence researchers construct systems and theories to perform or describe rational thought and action, criticizing and defending these systems and theories in terms similar to but more formal than those of the man or woman on the street. Judgments of human rationality commonly involve several different conceptions of rationality, including a logical conception used to judge thoughts, and an economic one used to judge actions or...

THE aim of this thesis is to investigate logical formalisms for describing, reasoning about, specifying, and perhaps ultimately verifying the properties of systems composed of multiple intelligent computational agents. There are two obvious resources available for this task. The first is the (largely AI) tradition of reasoning about the intentional notions (belief, desire, etc.). The second is the (mainstream computer science) tradition of temporal logics for reasoning about reactive systems. Unfortunately, neither resource is ideally suited to the task: most intentional logics have little to say on the subject of agent architecture, and tend to assume that agents are perfect reasoners, whereas models of concurrent systems from mainstream computer science typically deal with the execution of individual program instructions. This thesis proposes a solution which draws upon both resources. It defines a model of agents and multi-agent systems, and then defines two execution models, which ...

Understanding knowledge is a fundamental issue in many disciplines. In computer science, knowledge arises not only in the obvious contexts (such as knowledgebased systems), but also in distributed systems (where the goal is to have each processor "know" something, as in agreement protocols). A general semantic model of knowledge is introduced, to allow reasoning about statements such as "He knows that I know whether or not she knows whether or not it is raining." This approach more naturally models a state of knowledge than previous proposals (including Kripke structures). Using this notion of model, a model theory for knowledge is developed. This theory enables one to interpret the notion of a "finite amount of information". A preliminary version of this paper appeared in Proc. 25th IEEE Symp. on Foundations of Computer Science, 1984, pp. 268--278. This version is essentially identical to the version that appears in Journal of the ACM 38:2, 1991, pp. 382--428. y Part of th...

Introduction Pragmatics is commo,nly understood to be concerned with studying the relations between linguistic phenomena and properties of the context of use. The understanding of these relations is important in many areas of theoretical and applied research, from grammatical analysis to sociolinguistic field studies. One area where the importance of these relations has become particularly clear is the design of language understanding systems. Such systems are extremely limited, brittle, and unpractical if they do not have powerful ways to make use of contextual information in computing the meanings of utterances. The question of how this can be achieved in an effective and principled way forms one of the major obstacles in building such systems. Computational pragmatics, the study of how contextual information can be effectively brought to bear in language understanding and production processes, hopes to contribute to removing this obstacle. One way in which contextual infor

The notion of knowledge has recently acquired a great deal of importance in Computer Science, partly because of its importance in AI and expert systems, but also because of applications in distributed computing and possible, even likely relevance to cryptography. See [H2] for a representative collection of papers on knowledge. An important outstanding problem in the logic of knowledge is the problem of logical omniscience, which plagues all the current logics of knowledge that are founded on the notion of a possible world. We assume in these logics the notion of a world, or situation which is possible relative to an individual (knower) i. The individual i knows a fact B iff B is true in all the worlds which are possible for i. It follows that if i knows A and also knows A → B, then both A and A → B are true at all worlds possible for i and hence B is true at all the worlds possible for i so that i knows B. In particular, i knows all B which are logically valid. This principle, the principle of logical omniscience, is usually expressed as the formula Ki(A) ∧ Ki(A → B) → Ki(B) Now my knowledge may itself differ from world to world, and to deal with this fact

We study a new problem, the wakeup problem, that seems to be fundamental in distributed computing. We present efficient solutions to the problem and show how these solutions can be used to solve the consensus problem, the leader election problem, and other related problems. The main question we try to answer is, how much memory is needed to solve the wakeup problem? We assume a model that captures important properties of real systems that have been largely ignored by previous work on cooperative problems.

A model of information structure and common knowledge is presented which does not take states of the world as primitive. Rather, these states are constructed as sets of propositions, including propositions which describe knowledge. In this model information structure and measurability structure are endogenously defined in terms of the relation between the propositions. In particular, when agents are ignorant of their own ignorance, the information structure is not a partition of the state space. We show that Aumann’s (Ann. Statist. 4 (1976), 1236-1239) famous result on the impossibility of agreeing to disagree, which was proved for partitions, can be extended to such information structures. Journal of Economic Literature Classification Numbers: 021, 026. 0 1990 Academic press, hc. 1.