Testing BDI-based multi-agent systems using discrete event simulation

IF 2.6 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Martina Baiardi, Samuele Burattini, Giovanni Ciatto, Danilo Pianini
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引用次数: 0

Abstract

Multi-agent systems are designed to deal with open, distributed systems with unpredictable dynamics, which makes them inherently hard to test. The value of using simulation for this purpose is recognized in the literature, although achieving sufficient fidelity (i.e., the degree of similarity between the simulation and the real-world system) remains a challenging task. This is exacerbated when dealing with cognitive agent models, such as the Belief Desire Intention (BDI) model, where the agent codebase is not suitable to run unchanged in simulation environments, thus increasing the reality gap between the deployed and simulated systems. We argue that BDI developers should be able to test in simulation the same specification that will be later deployed, with no surrogate representations. Thus, in this paper, we discuss how the control flow of BDI agents can be mapped onto a Discrete Event Simulation (DES), showing that such integration is possible at different degrees of granularity. We substantiate our claims by producing an open-source prototype integration between two pre-existing tools (JaKtA and Alchemist), showing that it is possible to produce a simulation-based testing environment for distributed BDI agents, and that different granularities in mapping BDI agents over DESs may lead to different degrees of fidelity.

使用离散事件仿真测试基于bdi的多智能体系统
多代理系统设计用于处理具有不可预测动态的开放、分布式系统,这使得它们本质上难以测试。在文献中认识到为此目的使用仿真的价值,尽管实现足够的保真度(即仿真与现实世界系统之间的相似程度)仍然是一项具有挑战性的任务。在处理认知代理模型(如信念-愿望-意图(Belief - Desire - Intention, BDI)模型)时,这种情况会加剧,因为在认知代理模型中,代理代码库不适合在仿真环境中不加改变地运行,从而增加了部署系统和仿真系统之间的现实差距。我们认为BDI开发人员应该能够在模拟中测试稍后将部署的相同规范,而不需要代理表示。因此,在本文中,我们讨论了如何将BDI代理的控制流映射到离散事件模拟(DES)上,并表明这种集成在不同的粒度程度上是可能的。我们通过在两个预先存在的工具(JaKtA和Alchemist)之间生成一个开源原型集成来证实我们的主张,表明可以为分布式BDI代理生成一个基于模拟的测试环境,并且在DESs上映射BDI代理的不同粒度可能导致不同程度的保真度。
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来源期刊
Autonomous Agents and Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems 工程技术-计算机:人工智能
CiteScore
6.00
自引率
5.30%
发文量
48
审稿时长
>12 weeks
期刊介绍: This is the official journal of the International Foundation for Autonomous Agents and Multi-Agent Systems. It provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Coverage in Autonomous Agents and Multi-Agent Systems includes, but is not limited to: Agent decision-making architectures and their evaluation, including: cognitive models; knowledge representation; logics for agency; ontological reasoning; planning (single and multi-agent); reasoning (single and multi-agent) Cooperation and teamwork, including: distributed problem solving; human-robot/agent interaction; multi-user/multi-virtual-agent interaction; coalition formation; coordination Agent communication languages, including: their semantics, pragmatics, and implementation; agent communication protocols and conversations; agent commitments; speech act theory Ontologies for agent systems, agents and the semantic web, agents and semantic web services, Grid-based systems, and service-oriented computing Agent societies and societal issues, including: artificial social systems; environments, organizations and institutions; ethical and legal issues; privacy, safety and security; trust, reliability and reputation Agent-based system development, including: agent development techniques, tools and environments; agent programming languages; agent specification or validation languages Agent-based simulation, including: emergent behavior; participatory simulation; simulation techniques, tools and environments; social simulation Agreement technologies, including: argumentation; collective decision making; judgment aggregation and belief merging; negotiation; norms Economic paradigms, including: auction and mechanism design; bargaining and negotiation; economically-motivated agents; game theory (cooperative and non-cooperative); social choice and voting Learning agents, including: computational architectures for learning agents; evolution, adaptation; multi-agent learning. Robotic agents, including: integrated perception, cognition, and action; cognitive robotics; robot planning (including action and motion planning); multi-robot systems. Virtual agents, including: agents in games and virtual environments; companion and coaching agents; modeling personality, emotions; multimodal interaction; verbal and non-verbal expressiveness Significant, novel applications of agent technology Comprehensive reviews and authoritative tutorials of research and practice in agent systems Comprehensive and authoritative reviews of books dealing with agents and multi-agent systems.
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