Plan and Intent Recognition in a Multi-agent System for Collective Box Pushing

AbstractIn a distributed multi-agent system, an idle agent may be available to assist other agents in the system. An agent architecture called intent recognition is proposed in this article to accomplish this with minimal communication. To assist other agents in the system, an agent performing recognition observes the tasks other agents are performing. Unlike the much-studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. In this study, the key research question is: What are intent-recognition systems and how can these be used to have agents autonomously assist each other effectively and efficiently? A conceptual framework is proposed to address this question. An implementation of the conceptual framework is tested and evaluated. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. This research shows that under certain conditions, an intent-recognition system is more efficient than a plan recognition system.

Download Full-text

INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141459006x ◽

2014 ◽

Vol 28 (05) ◽

pp. 1459006

Author(s):

NAJLA AHMAD ◽

ARVIN AGAH

Keyword(s):

Conceptual Framework ◽

Recognition System ◽

Plan Recognition ◽

Multi Agent System ◽

Multi Agent Systems ◽

Agent System ◽

Intent Recognition ◽

Domain Specific ◽

Novel Approach ◽

Multi Agent

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

Download Full-text

Towards a Systematic Conflict Resolution Policy in Multi-agent System: A Conceptual Framework

Lecture Notes in Computer Science - Computer Supported Cooperative Work in Design II ◽

10.1007/11686699_28 ◽

2006 ◽

pp. 274-283

Author(s):

Yi Jiao ◽

Baifeng Wu ◽

Kun Zhu ◽

Qiang Yu

Keyword(s):

Conflict Resolution ◽

Conceptual Framework ◽

Multi Agent System ◽

Agent System ◽

System A ◽

Multi Agent

Download Full-text

ENHANCING MAS ENVIRONMENTS WITH ORGANIZATIONAL MECHANISMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000395 ◽

2011 ◽

Vol 20 (04) ◽

pp. 663-690 ◽

Cited By ~ 4

Author(s):

SERGIO ESPARCIA ◽

ESTEFANÍA ARGENTE ◽

ROBERTO CENTENO ◽

RAMÓN HERMOSO

Keyword(s):

Health Care ◽

Conceptual Framework ◽

Formal Model ◽

Multi Agent System ◽

Agent System ◽

Coordination System ◽

Multi Agent ◽

Organizational Mechanisms

This work proposes a new coordination system for the environment of a Multi-Agent System by merging the features from two important contributions to this field of research, Organizational Mechanisms and Artifacts. Organizational mechanisms can be introduced into a Multi-Agent System with the aim of influencing the behavior of agents populating it to achieve their goals in a proper way. In this paper, we propose to model organizational mechanisms by means of artifacts, which are non-proactive entities used by agents. Artifacts were presented within the Agents & Artifacts conceptual framework, and that present good advantages for coordinating agents' environments. We put forward a formal model that defines how organizational mechanisms can be designed by using artifacts theory. We validate the approach by presenting a case study focused on a real health care domain problem. Additionally, the Artifacts for Organizational Mechanisms are compared with some different proposed artifacts.

Download Full-text

A MULTI-AGENT SYSTEM ARCHITECTURE FOR DISTRIBUTED COMPUTER VISION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213000000203 ◽

2000 ◽

Vol 09 (02) ◽

pp. 305-319 ◽

Cited By ~ 9

Author(s):

THORSTEN GRAF ◽

ALOIS KNOLL

Keyword(s):

Computer Vision ◽

System Architecture ◽

Vision System ◽

Recognition Task ◽

Recognition System ◽

Multi Agent System ◽

Vision Systems ◽

Agent System ◽

Agent Architectures ◽

Multi Agent

We present a multi-agent system architecture dedicated to model computer vision systems, which provides the vision system with a great degree of flexibility. The basic idea of this architecture is to model a vision system as a society of autonomous agents, where each agent is responsible for specific vision tasks, the control strategy of a vision system is decentralized, and agents communicate using a flexible but easy understandable communication language. This directly leads to self-organizing vision systems, which accomplish vision tasks by goal-driven communication processes. We describe in detail the basic concepts of the proposed multi-agent system approach including the agent architectures, the communication language and network, as well as the interaction strategies. As a testbed for the proposed architecture we have modeled an object recognition system as an assembly of agents which organize themselves according to a given recognition task by employing communication.

Download Full-text