=Paper=
{{Paper
|id=Vol-223/paper-40
|storemode=property
|title=Exploiting the Environment for Coordinating Agent Intentions
|pdfUrl=https://ceur-ws.org/Vol-223/22.pdf
|volume=Vol-223
|authors=Tom Holvoet (K.U.Leuven),Paul Valckenaers (K.U.Leuven)
|dblpUrl=https://dblp.org/rec/conf/eumas/HolvoetV06
}}
==Exploiting the Environment for Coordinating Agent Intentions==
https://ceur-ws.org/Vol-223/22.pdf
EXPLOITING THE ENVIRONMENT FOR COORDINATING
AGENT INTENTIONS
Tom Holvoeta Paul Valckenaersb
a
Department of Computer Science, Katholieke Universiteit Leuven, Belgium,
paul.valckenaers@mech.kuleuven.be,
b
Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium,
paul.valckenaers@mech.kuleuven.be,
Abstract
One interesting family of MAS applications is characterized (1) by their large scale in terms of
number of agents and physical distribution, (2) by their very dynamic nature and (3) by their complex
functional and non-functional requirements. This family includes a.o. manufacturing control, traffic
control and web service coordination. For this family, the complexity of the software for the individual
agents using traditional BDI-approaches is overwhelming. In this paper, we present an innovative
approach to BDI which alleviates agent complexity through “delegate MASs”, which use the
environment and its resources to obtain BDI functionality. Delegate MASs consist of light-weight
agents, which are issued either by resources for building and maintaining information on the
environment, or by task agents in order to explore the options on behalf of the agents and to coordinate
their intentions. We describe the approach, and validate it in a case study of manufacturing control. The
evaluation in this case study shows the feasibility of the approach in coping with the large scale of the
application and shows that the approach elegantly achieves flexibility in highly dynamic environments.
This paper is a two-page discussion introducing a more extensive paper, accepted for publication in the
E4MAS 2006 postproceedings .
1 Introduction
BDI agents have internal representations of their beliefs, desires and intentions. A challenging aspect of
the applications addressed in this paper is that the community of agents has to account for the internal
states of other agents in an n-n fashion (multiple agents affect multiple agents in non-trivial manners).
Explicit and comprehensive information exchange through direct interaction amongst the agents leads
to information and communication overload, especially if planning into the (near) future is required.
The research in this paper uses the environment as a medium to account for this: the agents
delegate to the environment parts and aspects of the representation of their beliefs and their intentions.
This results in a normalization in which information and knowledge is represented only once
throughout the multi-agent system, and in which the representations benefit from the knowledge of
multiple sources.
By locating (part of) their beliefs in the environment, agents avoid exposure to specific and
dynamic properties of the world-of-interest. The delegate MAS is a mechanism, which guarantees
computational efficiency, for the agents to consult this decentralized and shared world model.
Dynamism is handled by evaporate-refresh mechanisms.
By propagating their intentions in the environment, agents introduce into the beliefs (world model)
of the affected agents, including themselves, the implications and consequences of their intentions.
Moreover, these intentions are transformed during this process into knowledge and information that
reflects not only what the originating agent knows but merges this with the knowledge in the
environment and even of affected agents. The propagation by means of a delegate MAS again
guarantees computational efficiency.
The remainder of this short introduction to the more extensive paper discusses the illustration in
multi-agent manufacturing control.
2 Delegate MAS for Manufacturing Control
The development and application of a delegate MAS has been pioneered for manufacturing control.
Figure 1 shows a simple case (for more information, see: www.mech.kuleuven.be/benchmarking). The
manufacturing system comprises four workstations, a rail-based transporter and a warehouse. The
GANTT-charts at the workstations are part of the externalized – delegated to the environment – beliefs
and intentions. They are short-term forecasts of the resource utilization, which have been constructed
based on intentions that were propagated by task agents.
Figure 1: The sample manufacturing system and the corresponding graph in the MAS environment.
Figure 2 shows how the lightweight ant agents of the delegate MAS travel along the graph in the
MAS environment. During their travel these agents query the local experts about expected behavior
and book time slots on the resources corresponding to the intentions of the agent that created them.
Figure 2 also shows how exploring ant agents use the environment as part of the beliefs of the agent
that created them. They observe and analyze the decentralized and shared world model to discover
possible and attractive ways to execute manufacturing tasks.
Figure 2: The delegate MAS propagating intentions and exploring for solutions respectively.
By repeating the exploration and intention propagation activities unremittingly, the delegate MAS
observes the changes and disturbances in the system with a minor delay. This allows the control system
to handle the dynamics. Delegation ensures that the ant agents in a delegate MAS benefit from the local
self-knowledge of the environment entities and corresponding agents. This permits the MAS to handle
heterogeneity, which is a real challenge in manufacturing, very well.
Acknowledgements
This paper presents work funded by the Research Fund of the K.U.Leuven (Concerted Research Action
on Autonomic Computing for Distributed Production Systems).