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

We present the fl-calculus, a computational calculus for higher-order concurrent programming. The calculus can elegantly express higher-order functions (both eager and lazy) and concurrent objects with encapsulated state and multiple inheritance. The primitives of the fl-calculus are logic variables, names, procedural abstraction, and cells. Cells provide a notion of state that is fully compatible with concurrency and constraints. Although it does not have a dedicated communication primitive, the fl-calculus can elegantly express one-to-many and many-to-one communication. There is an interesting relationship between the fl-calculus and the ß-calculus: The fl-calculus is subsumed by a calculus obtained by extending the asynchronous and polyadic ß-calculus with logic variables. The fl-calculus can be extended with primitives providing for constraint-based problem solving in the style of logic programming. A such extended fl-calculus has the remarkable property that it combines first-or...

The Mars system is described which models cooperative scheduling within a society of shipping companies as a multiagent system. Emphasis is placed on the functionality of the system as a whole--- the solution of the global scheduling problem emerges from local decision-making and problem-solving strategies. An extension of the contract net protocol is presented; we show that it can be used to obtain good initial solutions for complex resource allocation problems. By introducing global information based upon auction protocols, this initial solution can be improved significantly. Experimental results are provided evaluating the performance of different cooperative scheduling strategies. Although the concepts for resource scheduling are presented solely for the transportation domain, their abstraction is useful for a broad variety of resource allocation problems. The Mars system solves the dynamic scheduling problem where no complete specification of the problem is available a priori; thus, it is designed as an on-line system based upon anytime algorithms.

The paper presents an extension of the concurrent constraint model providing for higher-order programming, deep guards, and encapsulated search. The paper focuses on a higher-order combinator providing for encapsulated search. The search combinator spawns a local computation space and resolves remaining choices by returning the alternatives as first-class citizens. The search combinator allows to program different search strategies, including depth-first, indeterministic one solution, demand-driven multiple solution, all solutions, and best solution (branch and bound) search. The described computation model is realized in Oz, a programming language and system under development at DFKI. Keywords Concurrent constraint programming, higher-order programming, encapsulated search, search strategies, Oz. 1 Introduction Oz [2, 3, 9, 8, 7, 1] is an attempt to create a high-level concurrent programming language providing the problem solving capabilities of logic programming (i.e., constraints ...

Oz is an attempt to create a high-level concurrent programming language providing the problem solving capabilities of logic programming (i.e., constraints and search). Its computation model can be seen as a rather radical extension of the concurrent constraint model providing for higher-order programming, deep guards, state, and encapsulated search. This paper focuses on the most recent extension, a higher-order combinator providing for encapsulated search. The search combinator spawns a local computation space and resolves remaining choices by returning the alternatives as first-class citizens. The search combinator allows to program different search strategies, including depth-first, indeterministic one solution, demand-driven multiple solution, all solutions, and best solution (branch and bound) search. The paper also discusses the semantics of integer and finite domain constraints in a deep guard computation model.

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

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In this paper, we set up a unifying perspective of the individual control layers of the architecture InteRRaP for autonomous interacting agents. InteRRaP is a pragmatic approach to designing complex dynamic agent societies, e.g. for robotics [Muller & Pischel 94a] and cooperative scheduling applications [Fischer et al. 94]. It is based on three general functions describing how the actions an agent commits to are derived from its perception and from its mental model: belief revision and abstraction, situation recognition and goal activation, and planning and scheduling. It is argued that each InteRRaP control layer --- the behaviour-based layer, the local planning layer, and the cooperative planning layer --- can be described by a combination of different instantiations of these control functions. The basic structure of a control layer is defined. The individual functions and their implementation in the different layers are outlined. We demonstrate various options for the d...