Francisco Triguero

Ontologies and agents for a bus fleet management system

This paper reports on the design and implementation of a multi-agent decision support system for the bus fleet management domain. In particular, we show a complete description of the proposed multi-agent architecture and focus mainly on knowledge and software engineering features. The system has been developed in collaboration with the Malaga urban bus company (EMT) and it is based on a faithful reproduction of the real operating conditions of three existing lines of this company.

MULTI-AGENT SYSTEMS FOR DECISION SUPPORT: A CASE STUDY IN THE TRANSPORTATION MANAGEMENT DOMAIN

This article describes how agent and knowledge technology can be used to build advanced software systems that support operational decision making in complex domains. In particular, we present an abstract architecture and design guidelines for agent-based decision support systems. We illustrate our approach with a case study in the transportation management domain.

Agent coordination for bus fleet management

This paper presents an abstract multiagent architecture useful for Decision Support Systems (DSS), and shows how this architecture can be instantiated to a particular real-world domain, Bus Fleet Management (BFM). After discussing the benefits of adopting a multiagent approach for DSS, the paper introduces the real world case study and analyzes it. Next, an abstract MAS architecture for DSS in general, and specialized then for BFM case study is presented. The architecture includes a set of different agents which are in charge of different social roles, and what is needed for their coordination, the Coordination Facilitator and the coordination protocols. The paper is concentrated in this point showing a concrete example in the BFM domain.

A robust deception-free coalition formation model

We study two properties of coalition formation algorithms, very important for their application in real-life scenarios: robustness and tolerance to some agent misbehaviors. The study is performed for a previously proposed coalition formation model-based upon game theory for a class of task-oriented problems that guarantees an optimum task allocation and a stable (fair) profit division. The results show acceptable behavior and performance.

COALITIONS AMONG INTELLIGENT AGENTS: A TRACTABLE CASE

Coalition formation is an important mechanism for cooperation in multiagent systems. In this paper we address the problem of coalition formation among self‐interested agents in superadditive task‐oriented domains. We assume that each agent has some “structure,” i.e., that it can be described by the values taken by a set of m nonnegative attributes that represent the resources w each agent is endowed with. By defining the coalitional value as a function V of w, we prove a sufficient condition for the existence of a stable payment configuration—in the sense of the core—in terms of certain properties of V. We apply these ideas to a simple case that can be described by a linear program and show that it is possible to compute for it—in polynomial time—an optimal task allocation and a stable payment configuration.

Experiments on robustness and deception in a coalition formation model

In the last few years coalition formation algorithms have been proposed as a possible way of modeling autonomous agent cooperation in multi‐agent systems. This work is based on a previously proposed coalition formation model founded on game theory for a class of task‐oriented problems that guarantees an optimum task allocation and a stable profit division. In this paper we study two properties of the model that are very important for application in real‐life scenarios: robustness and tolerance to an agents misbehavior. First, we study the robustness of this model as regards the effect the agents failure has on the resultant profits of the coalition formation. Secondly, we also study the coalition formation model in the presence of misbehaving agents. Agents have some kind of execution autonomy, and they can deceive or mislead each other when they reveal their information, if they believe this will give them more profits. Copyright