Multi-agent system

About: Multi-agent system is a research topic. Over the lifetime, 27978 publications have been published within this topic receiving 465191 citations. The topic is also known as: multi-agent systems & multiagent system.

MCMAS : A model checker for Multi-Agent Systems

Abstract: This paper presents mcmas, a model checker for Multi-Agent Systems (MAS). Differently from traditional model checkers, mcmas permits the automatic verification of specifications that use epistemic, correctness, and cooperation modalities, in addition to the standard temporal modalities. These additional modalities are used to capture properties of various scenarios (including communication and security protocols, games, etc.) that may be difficult or unnatural to express with temporal operators only; a small number of applications are presented in Section[4]. Agents are described in mcmas by means of the dedicated programming language ISPL (Interpreted Systems Programming Language). The approach is symbolic and uses ordered binary decision diagrams (obdds), thereby extending standard techniques for temporal logic to other modalities distinctive of agents. mcmas and all the examples presented in this paper are available for download [14] under the terms of the GPL license.

A negotiation-based Multi-agent System for Supply Chain Management

Abstract: This paper describes an ongoing effort in developing a Multiagent System (MAS) for supply chain management. In our framework, functional agents can join in, stay, or leave the system. The Supply Chain Management System (SCMS) functionality is implemented through agent-based negotiation. When an order arrives, a virtual supply chain may emerge from the system through automated or semi-automated negotiation processes between functional agents. We present our framework and describe a number of negotiation performatives, which can be used to construct pair-wise and third party negotiation protocols for functional agent cooperation. We also explain how to formally model the negotiation process by using Colored Petri Nets (CPN) and we provide an example of establishing a virtual chain by solving a distributed constraint satisfaction problem.

An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans

Abstract: Agent integration architectures enable a heterogeneous, distributed set of agents to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform real-world tasks in a large-scale system remains difficult, especially due to three main challenges: ensuring robust execution in the face of a dynamic environment, providing abstract task specifications without all the low-level coordination details, and finding appropriate agents for inclusion in the overall system. To address these challenges, our Teamcore project provides the integration architecture with general-purpose teamwork coordination capabilities. We make each agent team-ready by providing it with a proxy capable of general teamwork reasoning. Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are built into its foundations by providing the proxies themselves with a teamwork model. Given this teamwork model, the Teamcore proxies addresses the first agent integration challenge, robust execution, by automatically generating the required coordination actions for the agents they represent. We can also exploit the proxies' reusable general teamwork knowledge to address the second agent integration challenge. Through team- oriented programming, a developer specifies a hierarchical organization and its goals and plans, abstracting away from coordination details. Finally, KARMA, our Knowledgeable Agent Resources Manager Assistant, can aid the developer in conquering the third agent integration challenge by locating agents that match the specified organization's requirements. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes and automates consistent teamwork among agents with some coordination capabilities. Thus, team-oriented programming provides a level of abstraction that can be used on top of previous approaches to agent-oriented programming. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated rehearsal of a military evacuation mission and facilitation of human collaboration.

MCMAS: a model checker for multi-agent systems

Abstract: This paper presents mcmas, a model checker for Multi-Agent Systems (MAS). Differently from traditional model checkers, mcmas permits the automatic verification of specifications that use epistemic, correctness, and cooperation modalities, in addition to the standard temporal modalities. These additional modalities are used to capture properties of various scenarios (including communication and security protocols, games, etc.) that may be difficult or unnatural to express with temporal operators only; a small number of applications are presented in Section[4]. Agents are described in mcmas by means of the dedicated programming language ISPL (Interpreted Systems Programming Language). The approach is symbolic and uses ordered binary decision diagrams (obdds), thereby extending standard techniques for temporal logic to other modalities distinctive of agents. mcmas and all the examples presented in this paper are available for download [14] under the terms of the GPL license.