Coalition formation with dynamically changing externalities

We consider multiple self-interested bounded-rational agents each of which has a goal it needs to achieve. Goals are achievable by executing a set of interdependent tasks. Some tasks exhibit time dependencies and may require sequential execution. For each agent, there may be several alternative sets of tasks that can achieve the goal. Execution of alternatives, may be more beneficial when done by a group of agents and not by a single agent. To jointly achieve goals, agents may form interdependent coalitions. Such coalition formation is computationally intractable. We nevertheless seek a practical solution that is not necessarily optimal yet acceptable by the agents. A solution where agents examine only coalitions in which they are members is inapplicable, as externalities are a major factor given task interdependencies. In this paper we study this coalition formation problem. We describe the problem and introduce a novel Multi-lateral Negotiation Protocol (MNP) that solves it by forming interdependent coalitions. We allow agents to heuristically make gradual concessions, revise their proposals and converge on specific alternatives, and nevertheless increase their expected gains

A multiagent multilateral negotiation protocol for joint decision-making

International audienc

A Multi Agent Scheduling Integrating Planning and Maintenance for Generalized Floor Shops

International audienceMulti-agent scheduling offers reactivity and distributed decision-making for floor shop control. Agents, which may represent any entities that act in production, negotiate to find best schedules. In this paper, we present a new multi-agent scheduling method that integrates both planning and maintenance activities. Actually, more than one plan can be generated for a job production. We suppose that plan selection must depend on information about machines maintenance and states to offer realistic schedules. Tests demonstrate that despite increasing time resolution with the agents’ number, our system is scalable with reduced Cmax and machines failure risk

A multiagent multilateral negotiation protocol for joint decision-making

International audienc

A Multi Agent Scheduling Integrating Planning and Maintenance for Generalized Floor Shops

International audienceMulti-agent scheduling offers reactivity and distributed decision-making for floor shop control. Agents, which may represent any entities that act in production, negotiate to find best schedules. In this paper, we present a new multi-agent scheduling method that integrates both planning and maintenance activities. Actually, more than one plan can be generated for a job production. We suppose that plan selection must depend on information about machines maintenance and states to offer realistic schedules. Tests demonstrate that despite increasing time resolution with the agents’ number, our system is scalable with reduced Cmax and machines failure risk

Experience and prospects for various control strategies for self-replicating multi-agent systems

Distributed cooperative applications (e.g., e-commerce) are now increasingly being designed as a set of autonomous entities, named agents, which interact and coordinate (thus named a multi-agent system). Such applications are often very dynamic: new agents can join or leave, they can change roles, strategies, etc. This high dynamicity creates new challenges to the traditional approaches of fault-tolerance. As relative importance of agents may evolve during the course of computation and problem solving, we need to dynamically and automatically identify the most critical agents and to adapt their replication strategies (e.g., active or passive, number of replicas...), in order to maximize their reliability and their availability. One important issue is then: what kind of information could be used to estimate which agents are most critical agents. In this paper, we will introduce our prototype architecture for adaptive replication. Notably, we will discuss various kinds of information and strategies to estimate criticality of agents: static dependences, dynamic dependences, roles, norms, plans. Some preliminary measurements and future directions will also be presented