Using BDI agents to improve driver modelling in a commuter scenario |
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| Authors: | Rosaldo J. F. Rossetti Rafael H. Bordini Ana L. C. Bazzan Sergio Bampi Ronghui Liu Dirck Van Vliet |
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| Affiliation: | 1. Department of Obstetrics and Gynecology, Program in Women''s Oncology, Warren Alpert Medical School of Brown University, Women and Infants Hospital, 101 Dudley Street, Providence, RI, USA;2. Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The University of North Carolina at Chapel Hill, Campus Box 7572, Chapel Hill, NC 27599-7572, USA;3. Department of Pathology, Warren Alpert Medical School of Brown University, Women and Infants Hospital, 101 Dudley Street, Providence, RI, USA;1. Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Avenue, CHS 43-264, Los Angeles, CA 90095, USA;2. Molecular Toxicology Interdepartmental Program, University of California, Los Angeles, USA;3. Molecular Biology Institute, University of California, Los Angeles, USA |
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| Abstract: | The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents. |
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| Keywords: | Multi-agent systems BDI agents Driver decision making Variable demand Intelligent transportation systems Traveller information systems Microscopic traffic simulation |
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