6,309 research outputs found

    Software Agents

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    being used, and touted, for applications as diverse as personalised information management, electronic commerce, interface design, computer games, and management of complex commercial and industrial processes. Despite this proliferation, there is, as yet, no commonly agreed upon definition of exactly what an agent is — Smith et al. (1994) define it as “a persistent software entity dedicated to a specific purpose”; Selker (1994) takes agents to be “computer programs that simulate a human relationship by doing something that another person could do for you”; and Janca (1995) defines an agent as “a software entity to which tasks can be delegated”. To capture this variety, a relatively loose notion of an agent as a self-contained program capable of controlling its own decision making and acting, based on its perception of its environment, in pursuit of one or more objectives will be used here. Within the extant applications, three distinct classes of agent can be identified. At the simplest level, there are “gopher ” agents, which execute straightforward tasks based on pre-specified rules and assumptions (eg inform me when the share price deviates by 10 % from its mean position or tell me when I need to reorder stock items). The next level of sophistication involves “service performing” agents, which execute a well defined task at the request of a user (eg find me the cheapest flight to Paris or arrange a meeting with the managing director some day next week). Finally, there are “predictive ” agents, which volunteer information or services to a user, without being explicitly asked, whenever it is deemed appropriate (eg an agent may monitor newsgroups on the INTERNET and return discussions that it believes to be of interest to the user or a holiday agent may inform its user that a travel firm is offering large discounts on holidays to South Africa knowing that the user is interested in safaris). Common to all these classes are the following key hallmarks of agenthoo

    Pitfalls of Agent-Oriented Development

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    While the theoretical and experimental foundations of agent-based systems are becoming increasingly well understood, comparatively little effort has been devoted to understanding the pragmatics of (multi-) agent systems development - the everyday reality of carrying out an agent-based development project. As a result, agent system developers are needlessly repeating the same mistakes, with the result that, at best, resources are wasted - at worst, projects fail. This paper identifies the main pitfalls that await the agent system developer, and where possible, makes tentative recommendations for how these pitfalls can be avoided or rectified

    On the Identification of Agents in the Design of Production Control Systems

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    This paper describes a methodology that is being developed for designing and building agent-based systems for the domain of production control. In particular, this paper deals with the steps that are involved in identifying the agents and in specifying their responsibilities. The methodology aims to be usable by engineers who have a background in production control but who have no prior experience in agent technology. For this reason, the methodology needs to be very prescriptive with respect to the agent-related aspects of design

    Multi-player games with LDL goals over finite traces

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    Linear Dynamic Logic on finite traces (LDLF) is a powerful logic for reasoning about the behaviour of concurrent and multi-agent systems. In this paper, we investigate techniques for both the characterisation and verification of equilibria in multi-player games with goals/objectives expressed using logics based on LDLF. This study builds upon a generalisation of Boolean games, a logic-based game model of multi-agent systems where players have goals succinctly represented in a logical way. Because LDLF goals are considered, in the settings we study—Reactive Modules games and iterated Boolean games with goals over finite traces—players' goals can be defined to be regular properties while achieved in a finite, but arbitrarily large, trace. In particular, using alternating automata, the paper investigates automata-theoretic approaches to the characterisation and verification of (pure strategy Nash) equilibria, shows that the set of Nash equilibria in multi-player games with LDLF objectives is regular, and provides complexity results for the associated automata constructions

    Misallocation, Access to Finance, and Public Credit: Firm-Level Evidence

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    Using a database of 23,000 firms in 45 economies, we test the quantitative importance of access to finance and access to public and private credit for the determination of misallocation. We first derive measures of factor market and size distortions, and then use these measures within a regression framework to test the significance of self-declared access-to-finance obstacles as well as the effect of access to a credit line issued by either a government-owned or private bank. We find that access-to-finance obstacles and private credit increase the dispersion of distortions. Public credit has a very small effect. For firms that do not face financial obstacles, public credit increases the dispersion of distortions; for firms that face financial obstacles, it slightly decreases dispersion. Public credit does not appear to compensate for the distortions that exist in private credit markets. Quantitatively, however, financial variables explain a very small part of the dispersion of factor market and size distortions

    Nash equilibrium and bisimulation invariance

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    Game theory provides a well-established framework for the analysis of concurrent and multi-agent systems. The basic idea is that concurrent processes (agents) can be understood as corresponding to players in a game; plays represent the possible computation runs of the system; and strategies define the behaviour of agents. Typically, strategies are modelled as functions from sequences of system states to player actions. Analysing a system in such a setting involves computing the set of (Nash) equilibria in the concurrent game. However, we show that, with respect to the above model of strategies (arguably, the "standard" model in the computer science literature), bisimilarity does not preserve the existence of Nash equilibria. Thus, two concurrent games which are behaviourally equivalent from a semantic perspective, and which from a logical perspective satisfy the same temporal logic formulae, may nevertheless have fundamentally different properties (solutions) from a game theoretic perspective. Our aim in this paper is to explore the issues raised by this discovery. After illustrating the issue by way of a motivating example, we present three models of strategies with respect to which the existence of Nash equilibria is preserved under bisimilarity. We use some of these models of strategies to provide new semantic foundations for logics for strategic reasoning, and investigate restricted scenarios where bisimilarity can be shown to preserve the existence of Nash equilibria with respect to the conventional model of strategies in the computer science literature

    Incentive Engineering for Boolean Games

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    Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

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    Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/ machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology
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