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...

Task execution in multi-agent environments may require cooperation among agents. Given a set of agents and a set of tasks which they have to satisfy, we consider situations where each task should be attached to a group of agents that will perform the task. Task allocation to groups of agents is necessary when tasks cannot be performed by a single agent. However it may also be beneficial when groups perform more efficiently with respect to the single agents' performance. In this paper we present several solutions to the problem of task allocation among autonomous agents, and suggest that the agents form coalitions in order to perform tasks or improve the efficiency of their performance. We present efficient distributed algorithms with low ratio bounds and with low computational complexities. These properties are proven theoretically and supported by simulations and an implementation in an agent system. Our methods are based on both the algorithmic aspects of combinatorics and approximat...

After arguing about the crucial importance of trust for Agents and MAS, we provide a definition of trust both as a mental state and as a social attitude and relation. We present the mental ingredients of trust: its specific beliefs and goals, with special attention to evaluations and expectations. We show the relation between trust and the mental background of delegation. We explain why trust is a bet, and implies some risks, and analyse the most basic forms of non-social trust (reliance on objects and tools) to arrive at the more complex forms of social trust, based on morality and reputation. Finally we present a principled quantification of trust, based on its cognitive ingredients, and use this "degree of trust " as the basis for a rational decision to delegate or not to another agent. The paper is

Autonomous agents working in multi-agent environments may need to cooperate in order to fulfill tasks. Given a set of agents and a set of tasks which they have to satisfy, we consider situations where each task should be attached to a group of agents which will perform the task. The allocation of tasks to groups of agents is necessary when tasks cannot be performed by a single agent. It may also be useful to assign groups of agents to tasks when the group's performance is more efficient than the performance of single agents. In this paper we give an efficient solution to the problem of task allocation among autonomous agents, and suggest that the agents will form coalitions in order to perform tasks or improve the efficiency. We present a distributed algorithm with a low ratio bound and with a low computational complexity. Our algorithm is an any-time algorithm, it is simple, efficient and easy to implement. 1

. Autonomous agents are designed to reach goals that were pre-defined by their operators. An important way to execute tasks and to maximize payoff is to share resources and to cooperate on task execution by creating coalitions of agents. Such coalitions will take place if, and only if, each member of a coalition gains more if he joins the coalition than he could gain before. There are several ways to create such coalitions and to divide the joint payoff among the members. Variance in these methods is due to different environments, different settings in a specific environment, and different approaches to a specific environment with specific settings. In this paper we focus on the cooperative (super-additive) environment, and suggest two different algorithms for coalition formation and payoff distribution in this environment. We also deal with the complexity of both computation and communication of each algorithm, and we try to give designers some basic tools for developing agents for th...

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.

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...

Goal-satisfaction in multi-agent environments via coalition formation may be beneficial in cases where agents cannot perform goals by themselves or they do so inefficiently. Agent coalition formation typically requires that each agent must be a member of only one coalition. This may lead to a waste of resources and capabilities. Therefore, we present algorithms that lead agents to the formation of overlapping coalitions, where each coalition is assigned a goal. The algorithms we present are appropriate for agents working as a Distributed Problem Solving system in non-super-additive environments. They are any-time distributed algorithms with a low computational complexity and low ratio-bound. Content area: Cooperation and coordination Kraus is also affiliated with the Institute for Advanced Computer Studies, University of Maryland. This material is based upon work supported in part by the NSF under Grant No. IRI-9423967 and the Israeli Science Ministry grant No. 6288. i 1 Introducti...

Cooperation among autonomous agents has been discussed in the DAI community for several years. Papers about cooperation [6, 45], negotiation [33], distributed planning [5], and coalition formation [28, 48], have provided a variety of approaches and several algorithms and solutions to situations wherein cooperation is possible. However, the case of cooperation in large-scale multi-agent systems (MAS) has not been thoroughly examined. Therefore, in this paper we present a framework for cooperative goal-satisfaction in large-scale environments focusing on a low complexity physics-oriented approach. The multi-agent systems with which we deal are modeled by a physics-oriented model. According to the model, MAS inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. To enable implementation of the model, we provide a detailed algorithm to be used by a single agent within the system. The model and the algorithm are a...

Abstract: This work aims at demonstrating the inherent advantages of embracing a strong notion of social embodiment in designing a real-world robot control architecture with explicit “intelligent ” social behaviour between a collective of robots. It develops the current thinking on embodiment beyond the physical by demonstrating the importance of social embodiment. A social framework develops the fundamental social attributes found when more than one robot co-inhabit a physical space. The social metaphors of identity, character, stereotypes and roles are presented and implemented within a real-world social robot paradigm in order to facilitate the realisation of explicit social goals.