Designing Multiagent Decision Support System The Case of Transportation Management S. Ossowski, A. Fern´andez, J.M. Serrano Universidad Rey Juan Carlos Dpt. of Computer Science sossowski,afernand,jserrano@escet.urjc.es J.Z. Hern´andez, A.M. Garc´ıa-Serrano Universidad Polit´ecnica de Madrid Dpt. of Artificial Intelligence phernan,agarcia@dia.fi.upm.es 1. Introduction Modern Decision Support Systems (DSS) not only store large amounts of decision-relevant data, but also aim at assisting decision-makers in exploring the meaning of that data, so as to take decisions based on understanding. To this end, a distributed approach to the construction of DSS has become popular: decision-support agents are responsible for parts of the decision-making process in a (semi)autonomous (individually) rational fashion [2]. In this paper we outline an abstract multiagent architecture that provides design guidelines for this kind of systems. We sketch its application to two multiagent DSS for different realworld transportation management problems and summarise the lessons learnt from this enterprise.
2. An abstract architecture for multiagent DSS The key point in modern DSS is to interact with the decision-maker to assist her in exploring the implications of her judgments. So, in order to develop a generic organisational model of DSS, it is natural to set out from an analysis of typical decision support dialogues between DM and DSS. Based on their (macro-level) functionality, we have identified different types of communicative interactions. Various communicative roles participate in these interactions, and are characterised by the Communicative Actions (CA) that they perform, and the communication pro
Work supported by the Spanish Ministry of Science and Technology (MCyT) under grant TIC2000-1370-C04. The authors would like to thank the Public Works and Transport Dept. of the Local Government of Bizkaia (DFB) as well as the Malaga Local Transport Consortium (EMT) for their cooperation.
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J.L. P´erez-de-la-Cruz, M.V. Belmonte Universidad de M´alaga ETSI Inform´atica perez,mavi@lcc.uma.es J.M. Maseda LABEIN, Technological Centre Information Society Unit
[email protected] tocols they may engage in. Still, roles in agent-based DSS require domain competence as well, so we specialise communicative roles into social roles based on the elements of a domain ontology of which they inform, that they explain, etc [3]. Based on the social roles that we have identified previously, we have come up with the abstract multiagent DSS architecture shown in Fig.1. (1) Data agents (DA) play the informer role with respect to the current state of a certain part of the system. As such, they are in charge of information retrieval from different information sources like sensors or databases, and its distribution. (2) Action Implementation Agent (AIA) play the requestee role and are in charge of actually executing the actions that the DM has chosen to take. (3) Management Agents (MA) play the remaining informer roles as well as the advisor and explainer roles. They may rely on the services of (4) Coordination Facilitators (CF) that provide support for negotiation and matchmaking (recruiting, brokering) interactions. CF agents are particularly relevant in DSS for groups of decision-makers. (5) User Interface Agents (UIA) play the remaining social roles on behalf of the user. Finally, (5) Peripheral Agents (PA) represent the support infrastructure for the DSS.
Figure 1. Abstract DSS Architecture
Figure 2. Traffic Management UIA Figure 3. Screenshot of the BFM Simulator
3. DSS for Transportation Management We have applied our approach to two real-world transportation management domains in Spain: traffic control within part of the high-capacity road network in the Bilbao area, and bus fleet management (BFM) of parts of the public bus network in downtown Malaga. In the first case, traffic operators in the Bilbao mobility management centre receive information about the traffic state by means of loop detectors, and take decisions on the control actions to apply in order to solve or minimise congestions, e.g. by displaying messages on variable message panels installed above the road to warn drivers about traffic problems or recommend alternative routes. Our DSS prototype assists operators in their management task, helping them to configure consistent control plans for the whole road network, and exploiting adequately the available signal devices from a global perspective. For this purpose, the Bilbao trial zone has been subdivided into 12 overlapping so-called problem areas to support a better analysis and understanding of the causes and evolution of traffic problems. Each problem area is managed by a society of MA agents. Coordination among MAs is done in a decentraslised fashion based on conventions. Fig. 2 shows the UIA of our prototype. All in all, it comprises more than 20 FIPA compliant agents implemented on top of the JADE platform. In the second case, control centre operators of the Malaga local transport consortium interact with an exploitation support system that, based on GPS data, presents status information for each bus with regard to the scheduled services. Their decisions to adjust services to unforeseen circumstances are communicated by radio to the drivers. For our DSS protoype, we have implemented a simulator that, based on the actual bus schedules, emulates the exploitation support system (Fig. 3). The DAs and AIAs of our prototype are essentially wrappers for this simulator. There are several MAs, one for each line. In case of resource conflicts (e.g. different lines competing for a support vehicle), MAs rely on a CF enacting a market-mechanism for coordination.
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4. Conclusions The implementation of the DSS prototypes, that required the integration of various software technologies and tools (JADE, KSM [1], JESS, Prot´eg´e-II), has been initially complex but required a reasonable amount of programming work. We found that some communicative roles and interactions are not adequately supported by FIPA. As a result, we have developed a method to build principled extensions to ACLs (and FIPA ACL in particular) [4], as well as a set of software components that encapsulate the corresponding dialogical behaviour for its use by JADE agents, to be used in future applications. We are currently refining the knowledge models of the prototypes, so as to evaluate their performance. Future work comprises the integration of additional Peripheral Agents (e.g. to supply services of traffic management agents to the BFM system), and the use of mobile devices (e.g. onboard driver information systems).
References [1] J. Cuena, J. Hern´andez, and M. Molina. Knowledge-oriented design of an application for real time traffic management. In European Conference on Artificial Intelligence, pages 217– 245. Wiley & Sons, 1996. [2] J. Cuena and S. Ossowski. Distributed models for decision support. In Weiss, editor, Multi-Agent Systems - A Modern Approach to DAI. MIT Press, 1999. [3] S. Ossowski, J. P´erez de la Cruz, J. Hern´andez, J. Maseda, A. Fern´andez, M. Belmonte, A. Garc´ia Serrano, J. Serrano, R. Le´on, and F. Carbone. Towards a generic multiagent model for decision support. In Spanish Conference on Artificial Intelligence (CAEPIA’03), LNAI. Springer, 2004. [4] J. Serrano, S. Ossowski, and A. Fern´andez. The pragmatics of software agents – analysis and design of agent communication languages. In Klusch, Bergamaschi, Edwards, and Petta, editors, Intelligent Information Agents – An AgentLink Perspective, volume 2586 of LNAI, pages 234–274. Springer, 2003.
