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.

Agent-oriented techniques represent an exciting new means of analysing, designing and building complex software systems. They have the potential to significantly improve current practice in software engineering and to extend the range of applications that can feasibly be tackled. Yet, to date, there have been few serious attempts to cast agent systems as a software engineering paradigm. This paper seeks to rectify this omission. Specifically, it will be argued that: (i) the conceptual apparatus of agent-oriented systems is well-suited to building software solutions for complex systems and (ii) agent-oriented approaches represent a genuine advance over the current state of the art for engineering complex systems. Following on from this view, the major issues raised by adopting an agent-oriented approach to software engineering are highlighted and discussed. 1.

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

Software and knowledge... In this article, we argue that intelligent agents and agent-based systems offer novel opportunities for developing effective tools and techniques. Following a discussion on the classic subject of what makes software complex, we introduce intelligent agents as software structures capable of making "rational decisions". Such rational decision-makers are well-suited to the construction of certain types of software, which mainstream software engineering has had little success with. We then go on to examine a number of prototype techniques proposed for engineering agent systems, including formal specification and verification methods for agent systems, and techniques for implementing agent specifications

Abstract. Software engineers continually strive to develop tools and techniques to manage the complexity that is inherent in software systems. In this article, we argue that intelligent agents and multi-agent systems are just such tools. We begin by reviewing what is meant by the term “agent”, and contrast agents with objects. We then go on to examine a number of prototype techniques proposed for engineering agent systems, including methodologies for agent-oriented analysis and design, formal specification and verification methods for agent systems, and techniques for implementing agent specifications. 1

Rational agents are important objects of study in several research communities, including economics, philosophy, cognitive science, and most recently computer science and artificial intelligence. Crudely, a rational agent is an entity that is capable of acting on its environment, and which chooses to act in such a way as to further its own best interests. There has recently been much interest in the use of mathematical logic for developing formal theories of such agents. Such theories view agents as practical reasoning systems, deciding moment by moment which action to perform nexi, given the beliefs they have about the world and their desires with respect to how they would like the world to be. In this article, we survey the state of the art in developing logical theories of rational agency. Following a discussion on the dimensions along which such theories can vary, we briefly survey the logical tools available in order to construct such theories. We then review and critically assess three of the best known theories of rational agency: Cohen and Levesque's intention logic, Rao and Georgeff's BDI logics, and the KARO framework of Meyer et al. We then discuss the various roles that such logics can play in helping us to engineer rational agents, and conclude with a discussion of open problems.

In this paper, I motivate, define, and illustrate the notion of computationally grounded theories of agency. A theory of agency is said to be computationally grounded if we can give the theory an interpretation in terms of some concrete computational model. This requirement is essential if we are to claim that the theories we develop can be understood as expressing properties of real multiagent systems. After introducing and formally defining the concept of a computationally grounded theory of agency, I illustrate the idea with reference to ¡£¢¥¤ logic, a formalism for reasoning about agent systems that has a semantics defined with respect to an automata-like model of agents. ¡£¢¦¤

Agent-Oriented Software Engineering (AOSE) is an emerging paradigm within industry that offers much potential of the management of the increasing levels of complexity inherent within modern software systems. For this paradigm to gain widespread acceptance, it is vital that we develop comprehensive frameworks that support the development and deployment of agent-oriented applications. This thesis contributes to this through the development of a four-tier development framework entitled the Agent Factory System. This framework combines an agent programming language that is founded upon a formal agent theory of commitment; a run-time environment that delivers a set of services that support the deployment of agent-oriented applications written in this programming language; an integrated development environment that delivers a toolkit that supports the development of these applications; and a development methodology that promotes a structure approach to the use of this toolkit. Finally, we evaluate the Agent Factory System from the context of various real-world

Abstract. The European Commission’s eEurope initiative aims to bring every citizen, home, school, business and administration online to create a digitally literate Europe. The value lies not in the objective itself, but in its ability to facilitate the advance of Europe into new ways of living and working. Just as in the first literacy revolution, our lives will change in ways never imagined. The vision of eEurope is underpinned by a technological infrastructure that is now taken for granted. Yet it provides us with the ability to pioneer radical new ways of doing business, of undertaking science, and, of managing our everyday activities. Key to this step change is the development of appropriate mechanisms to automate and improve existing tasks, to anticipate desired actions on our behalf (as human users) and to undertake them, while at the same time enabling us to stay involved and retain as much control as required. For many, these mechanisms are now being realised by agent technologies, which are already providing dramatic and sustained benefits in several business and industry domains, including B2B exchanges, supply chain management, car manufacturing, and so on. While there are many real successes of agent technologies to report, there is still much to be done in research and development for the full benefits to be achieved. This is especially true in the context of environments of pervasive computing devices that are envisaged in coming years. This paper describes the current state-of-the-art of agent technologies and

In this paper we define two logics, KL n and BL n , and present resolutionbased proof methods for both. KL n is a temporal logic of knowledge. Thus, in addition to the usual connectives of linear discrete temporal logic, it contains a set of unary modal connectives for representing the knowledge possessed by agents. The logic BL n is somewhat similar: it is a temporal logic that contains connectives for representing the beliefs of agents. The proof methods we present for these logics involve two key steps. First, a formula to be tested for unsatisfiability is translated into a normal form. Secondly, a family of resolution rules are used, to deal with the interactions between the various operators of the logics. In addition to a description of the normal form and the proof methods, we present some short worked examples and proposals for future work. 1