This paper describes a new multi-player computer game, Colored Trails (CT), which may be played by people, computers and heterogeneous groups. CT was designed to enable investigation of properties of decision-making strategies in multi-agent situations of varying complexity. The paper presents the results of an initial series of experiments of CT games in which agents ’ choices affected not only their own outcomes but also the outcomes of other agents. It compares the behavior of people with that of computer agents deploying a variety of decision-making strategies. The results align with behavioral economics studies in showing that people cooperate when they play and that factors of social dependency influence their levels of cooperation. Preliminary results indicate that people design agents to play strategies closer to game-theory predictions, yielding lower utility. Additional experiments show that such agents perform worse than agents designed to make choices that resemble human cooperative behavior. The paper describes challenges raised by these results for designers of agents, especially agents that need to operate in heterogeneous groups that include people. 1.

The allocation of resources within a system of autonomous agents, that not only have preferences over alternative allocations of resources but also actively participate in computing an allocation, is an exciting area of research at the interface of Computer Science and Economics. This paper is a survey of some of the most salient issues in Multiagent Resource Allocation. In particular, we review various languages to represent the preferences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation. We also discuss pertinent issues regarding allocation procedures and present important complexity results. Our presentation of theoretical issues is complemented by a discussion of software packages for the simulation of agent-based market places. We also introduce four major application areas for Multiagent Resource Allocation, namely industrial procurement, sharing of satellite resources, manufacturing control, and grid computing.

The use of agent systems as a means of implementing contract negotiation in e-commerce and e-trading environments has been the focus of considerable recent interest. A widely studied abstract model considers the setting in which a set of agents have some collection of resources shared out between them and attempt to construct a mutually beneficial optimal reallocation of these by trading resources. The simplest such trades are those in which a single agent transfers exactly one resource to another -- so-called `one-resource-ata -time' or `O-contracts'. In this research note we consider the computational complexity of a number of natural decision problems in this setting.

A multiagent system may be thought of as an artificial society of autonomous software agents and we can apply concepts borrowed from welfare economics and social choice theory negotiation framework where agents can agree on multilateral deals to exchange bundles of indivisible resources. We then analyse how these deals affect social welfare for different instances of the basic framework and different interpretations of the concept of social welfare itself. In particular, we show how certain classes of deals are both sufficient and necessary to guarantee that a socially optimal allocation of resources will be reached eventually. 1.

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

We examine properties of a model of resource allocation in which several agents exchange resources in order to optimise their individual holdings. The schemes discussed relate to well-known negotiation protocols proposed in earlier work and we consider a number of alternative notions of "rationality" covering both quantitative measures, e.g. cooperative and individual rationality and more qualitative forms, e.g. Pigou-Dalton transfers. While it is known that imposing particular rationality and structural restrictions on the form of exchanges may render these unable to realise every reallocation of the resource set, in this paper we address the issue of the number of restricted rational exchanges that may be required to implement a particular reallocation when it is possible to do so. We construct examples showing that this number may be exponential (in the number of resources m), even when all of the agent utility functions are monotonic. We further show that k agents may achieve in a single exchange a reallocation requiring exponentially many rational exchanges if at most k 1 agents can participate, this same reallocation being unrealisable by any sequences of rational exchanges in which at most k 2 agents are involved.

We briefly review previous work on the welfare engineering framework where autonomous software agents negotiate on the allocation of a number of discrete resources, and point out connections to combinatorial optimisation problems, including combinatorial auctions, that shed light on the computational complexity of the framework. We give particular consideration to scenarios where the preferences of agents are modelled in terms of k-additive utility functions, i.e. scenarios where synergies between different resources are restricted to bundles of at most k items. Key words: negotiation, representation of utility functions, social welfare, combinatorial optimisation, bidding languages for combinatorial auctions 1

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We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of discrete resources. In this framework, reaching an optimal allocation potentially requires very complex multilateral deals. Therefore, we are interested in identifying classes of utility functions such that any negotiation conducted by means of deals involving only a single resource at at time is bound to converge to an optimal allocation whenever all agents model their preferences using these functions. We show that the class of modular utility functions is not only sufficient but also maximal in this sense. 1

We study the complexity of a multilateral negotiation framework where autonomous agents agree on a sequence of deals to exchange sets of discrete resources in order to both further their own goals and to achieve a distribution of resources that is socially optimal. When analysing such a framework, we can distinguish different aspects of complexity: How many deals are required to reach an optimal allocation of resources? How many communicative exchanges are required to agree on one such deal? How complex a communication language do we require? And finally, how complex is the reasoning task faced by each agent? This paper presents a number of results pertaining, in particular, to the first of these questions.