One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparativ...

Abstract. Many autonomous agents operate in domains in which the cooperation of their fellow agents cannot be guaranteed. In such domains negotiation is essential to persuade others of the value of co-operation. This paper describes a general framework for negotiation in which agents exchange proposals backed by arguments which summarise the reasons why the proposals should be accepted. The argumentation is persuasive because the exchanges are able to alter the mental state of the agents involved. The framework is inspired by our work in the domain of business process management and is explained using examples from that domain. Keywords: Automated negotiation, Argumentation, Persuasion. 1

This paper presents a high-level framework for analysing and designing intelligent agents. The framework's key abstraction mechanism is a new computer level called the Social Level. The Social Level sits immediately above the Knowledge Level, as defined by Allen Newell, and is concerned with the inherently social aspects of multiple agent systems. To illustrate the working of this framework, an important new class of agent is identified and then specified. Socially responsible agents retain their local autonomy but still draw from, and provide resources to, the larger community. Through empirical evaluation, it is shown that such agents produce both good system-wide performance and good individual performance. 1. INTRODUCTION The number of multi-agent systems being designed and built is rapidly increasing as software agents gain acceptance as a powerful and useful technology for solving complex problems (Chaib-draa, 1995; Jennings, 1994; PAAM, 1996). As applications become more comple...

Most autonomous agents are situated in a social context and need to interact with other agents (both human and artificial) to complete their problem solving objectives. Such agents are usually capable of performing a wide range of actions and engaging in a variety of social interactions. Faced with this variety of options, an agent must decide what to do. There are many potential decision making functions which could be employed to make the choice. Each such function will have a different effect on the success of the individual agent and of the overall system in which it is situated. Therefore, this paper examines agents' decision making functions to ascertain their likely properties and attributes. A framework for characterising social decision making is presented and a socially responsible decision making principle is proposed which enables both the agent and the overall system to perform well. This principle is illustrated, and empirically evaluated, in a multiagent system for unloading lorries at a warehouse. 1.

The design of social laws for artificial agent societies is a basic approach to coordinating multi-agent systems. It exposes the spectrum between fully-centralized and fully-decentralized coordination mechanisms. Useful social laws set constraints on the agents' activities which allow them to work individually in a mutually compatible manner.

. The cooperation knowledge level is a new computer level specifically for multi-agent problem solvers which describes rich and explicit models of common social phenomena. A cooperation level description (called joint responsibility) is developed to describe how participants should behave during interactions in which groups of agents collaborate to solve a common problem. The utility of this model is highlighted in the real-world environment of electricity transport management in which agents have to make decisions using partial, imprecise views of the system and cope with the inherent dynamics of the environment. In such situations the tracking of social action becomes a primary consideration; joint responsibility provides evaluation criteria and the causal link to behaviour upon which such assessment can be based. Keywords: Multi-Agent Systems, Distributed AI, Joint Intentions, Knowledge Level 1. Introduction Sophisticated problem solving is based upon knowledge. In advanced syste...

An artificial social system is a set of restrictions on agents' behaviors in a multiagent environment. Its role is to allow agents to coexist in a shared environment and pursue their respective goals in the presence of other agents. This paper argues that artificial social systems exist in practically every multi-agent system, and play a major role in the performance and effectiveness of the agents. We propose artificial social systems as an explicit and formal object of study, and investigate several basic issues that arise in their design. Keywords: Social Laws, Multi-Agent Systems, Off-Line Design This work was supported in part by the US-Israel Binational Foundation. The work of the first author was supported by an Alon Fellowship, and by a Helen and Marcus Kimmelman Career Development Chair. The second author was supported in part by an Eshkol Fellowship of the Israeli Ministry of Science and Technology, and later by the Air Force Office of Scientific Research. Part of the resea...

The paper presents a model for the construction of an artificial agent that can express performatives through facial expression. The performative of a speech act or communicative act is the particular communicative intention a Sender has to one's Addressee, the way one wants to socially relate oneself to the interlocutor. Performatives are decomposed both on the meaning and on the signal side: on the meaning side, a performative is represented as a cluster of cognitive units, that in turn include subclusters mentioning the Sender's general goal (informing, asking, requesting), the power relationship between Sender and Addressee, the Sender's affective state, and further information peculiar of specific performatives; on the signal side, facial expressions are decomposed into Action Units. The proposed system computes the appropriate performative of one's communicative acts through consideration of the context of communication, particularly of the Addressee's cognitive capacity, social relationship and personality traits, and then expresses the computed performatives through 3D facial displays. Résumé Cet article présente un modèle pour la création d’un agent synthétique pouvant exprimer des performatifs à l’aide d’expressions faciales. Le performatif d’un acte parlé ou d’un acte communicatif est l’intention

A significant class of agent architectures designed for operation in a multi-agent world choose their next actions or plans based on a limited analysis

Abstract. Multi-agent system research is concerned with the issues surrounding the performance of collections of interacting agents. A major concern, therefore, is with the design of the decision making mechanism that the individual agents employ in order to determine which actions to take to achieve their goals. An attractive and much sought after property of this mechanism is that it produces decisions that are rational from the perspective of the individual agent. However, agents are also inherently social. Moreover, individual and social concerns often conflict, perhaps leading to inefficient performance of the individual and the system. To address these problems we propose a formal decision making framework, based on social welfare functions, that combines social and individual perspectives in a unified and flexible manner. The framework is realised in an exemplar computational setting and an empirical analysis is made of the relative performance of varyingly sociable decision making functions in a range of environments. 1