A novel approach to streamline RE process for multi-agent systems

2015 ◽  
Vol 1 (2/3/4) ◽  
pp. 190
Author(s):  
Anuja Soni ◽  
Vibha Gaur
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent

scholarly journals Counterplanning using Goal Recognition and Landmarks

2018 ◽  
Author(s):  
Alberto Pozanco ◽  
Yolanda E-Martín ◽  
Susana Fernández ◽  
Daniel Borrajo
Keyword(s):  
Automated Planning ◽  
Experimental Results ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Goal Recognition ◽  
Domain Independent

In non-cooperative multi-agent systems, agents might want to prevent the opponents from achieving their goals. One alternative to solve this task would be using counterplanning to generate a plan that allows an agent to block other's to reach their goals. In this paper, we introduce a fully automated domain-independent approach for counterplanning. It combines; goal recognition to infer an opponent's goal; landmarks' computation to identify subgoals that can be used to block opponents' goals achievement; and classical automated planning to generate plans that prevent the opponent's goals achievement. Experimental results in several domains show the benefits of our novel approach. 

scholarly journals A Novel Heterogeneous Swarm Reinforcement Learning Method for Sequential Decision Making Problems

2019 ◽  
Vol 1 (2) ◽  
pp. 590-610
Author(s):  
Zohreh Akbari ◽  
Rainer Unland
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Single Agent ◽  
Sequential Decision Making ◽  
Multi Agent Systems ◽  
Sequential Decision ◽  
Agent Systems ◽  
Novel Approach ◽  
Markov Decision ◽  
Multi Agent

Sequential Decision Making Problems (SDMPs) that can be modeled as Markov Decision Processes can be solved using methods that combine Dynamic Programming (DP) and Reinforcement Learning (RL). Depending on the problem scenarios and the available Decision Makers (DMs), such RL algorithms may be designed for single-agent systems or multi-agent systems that either consist of agents with individual goals and decision making capabilities, which are influenced by other agent’s decisions, or behave as a swarm of agents that collaboratively learn a single objective. Many studies have been conducted in this area; however, when concentrating on available swarm RL algorithms, one obtains a clear view of the areas that still require attention. Most of the studies in this area focus on homogeneous swarms and so far, systems introduced as Heterogeneous Swarms (HetSs) merely include very few, i.e., two or three sub-swarms of homogeneous agents, which either, according to their capabilities, deal with a specific sub-problem of the general problem or exhibit different behaviors in order to reduce the risk of bias. This study introduces a novel approach that allows agents, which are originally designed to solve different problems and hence have higher degrees of heterogeneity, to behave as a swarm when addressing identical sub-problems. In fact, the affinity between two agents, which measures the compatibility of agents to work together towards solving a specific sub-problem, is used in designing a Heterogeneous Swarm RL (HetSRL) algorithm that allows HetSs to solve the intended SDMPs.

Towards a New Approach for Controlling the Reorganization Process of Multi-Agent Systems

2014 ◽  
Vol 6 (2) ◽  
pp. 73-91
Author(s):  
Kalache Ayyoub ◽  
Farid Mokhati ◽  
Mourad Badri
Keyword(s):  
Control Process ◽  
Software Tool ◽  
Multi Agent Systems ◽  
Concrete Case ◽  
New Approach ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Computational Systems

Reorganization in Multi-Agent Systems plays a crucial role in the dynamic adaptation of the structure and the behaviour of organizations. In order to ensure consistency of the resulting organization, the reorganization process has to be controlled. This paper proposes a novel approach for controlling the reorganization process of Multi-Agent Systems, which are specified and implemented using the Framework OMACS (Organizational Model for Adaptive Computational Systems). The proposed control process is accomplished using the Framework MOP (Monitoring Oriented Programming) for supporting the verification of some reorganizational properties. The proposed approach, supported by a software tool that we developed, is illustrated using a concrete case study.

Towards a New Approach for Controlling the Reorganization Process of Multi-Agent Systems

2017 ◽  
pp. 2068-2087
Author(s):  
Kalache Ayyoub ◽  
Farid Mokhati ◽  
Mourad Badri
Keyword(s):  
Control Process ◽  
Software Tool ◽  
Dynamic Adaptation ◽  
Multi Agent Systems ◽  
New Approach ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Computational Systems

Reorganization in Multi-Agent Systems plays a crucial role in the dynamic adaptation of the structure and the behaviour of organizations. In order to ensure consistency of the resulting organization, the reorganization process has to be controlled. This paper proposes a novel approach for controlling the reorganization process of Multi-Agent Systems, which are specified and implemented using the Framework OMACS (Organizational Model for Adaptive Computational Systems). The proposed control process is accomplished using the Framework MOP (Monitoring Oriented Programming) for supporting the verification of some reorganizational properties. The proposed approach, supported by a software tool that we developed, is illustrated using a concrete case study.

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