Suzanne Pinson

A distributed decision support system for strategic planning

Abstract Recent advances in artificial intelligence, particularly in the field of multi-agent theory, offer great promises in modeling strategic planning processes. In this domain, the ability to introduce distinct cognitive agents which cooperate to solve the problem enables the processing of more complex and ill-structured problems. This paper presents a general framework for building a distributed strategic decision support system (DSDSS) which integrates both advances in distributed decision making and distributed artificial intelligence. It goes on to describe a cooperative and distributed system with two specific features: the users intervene as human agents in the solution formation, and strategic knowledge and domain knowledge are distributed in different agents which communicate through various blackboards and message passing. An example is provided in the field of strategic marketing which illustrates how the system operates.

Dynamic modeling of a disturbance in a multi-agent system for traffic regulation

This paper presents the modeling of a disturbance on a public transportation line. The proposed model allows the synthesis, evaluation and update of available information in order to help human regulators in their monitoring task. It begins with a formal modeling of the disturbance concept. This modeling makes it possible to capitalize the knowledge available within a monitoring station and to follow up the evolution of the disturbances in real time. The paper goes on to propose a multi-agent representation of an incident allowing the integration of the disturbance processing within the activity of a network system.

A multi-agent method for forming and dynamic restructuring of pareto optimal coalitions

The first part of this paper presents a coalition formation method for multi-agent systems which finds a Pareto optimal solution without aggregating the preferences of the agents. This protocol is adapted to problems requiring coordination by coalition formation, where it is undesirable, or not possible, to aggregate the preferences of the agents. The second part proposes an extension of this method enabling dynamic restructuring of coalitions when changes occur in the system.

Toward a Multi-agent Modelling Approach for Urban Public Transportation Systems

In this paper, a multi-agent system (MAS) for bus transportation management is presented. The aim of our MAS is 1) to diagnose problems in the bus lines (bus delays, bus advances,...) and 2) to detect inconsistency in positioning data sent by buses to the central operator. Our MAS behaves as a Multi-Agent Decision Support System (MADSS) used by human regulators in order to manage bus lines. In our model, buses and stops are modeled as autonomous agents that cooperate to detect faults (disturbances) in the transportation network. An original interaction model called ESAC (Environment as Active Communication Support) was designed to allow nonintentional as well as direct communication. The system was implemented using ILOG RULES and was tested on data coming from the Brussels bus transportation network (STIB).

An Agent-Based Simulation Testing the Impact of Water Allocation on Farmers' Collective Behaviors

Many negotiations take place between farmers, water suppliers, public servants, and environmentalists to allocate water resources between users in different areas. However, few negotiations quantify the consequences of alternatives solutions. Models that are used are often oversimplified and only take into account elements that are easy to calculate, or they are too complex to be used for negotiations in real time. In all cases, they do not consider the heterogeneity of decision makers. The authors demonstrate that agent-based modeling could help these negotiations by showing the consequences of water allocation rules with respect to different criteria.

A Formal Model of Communication and Context Awareness in Multiagent Systems

Awareness is a concept that has been frequently studied in the context of Computer Supported Cooperative Work. However, other fields of computer science can benefit from this concept. Recent research in the multi-agent systems field has highlighted the relevance of complex interaction models such as multi-party communication and context awareness for simulation and adaptive systems. In this article, we present a generic interaction model that enables to use these different models in a standardized way. Emerging as a first-order abstraction, the environment, in the sense of a common medium for the agents, is a suitable paradigm to support the agents’ awareness. We present an operational model, called Environment as Active Support of Interaction, to take into account all the agents that can be interested in a communication. This model is then extended for the regulation of multiagent systems interactions. Priority policies are given to manage the rules governing the context (un-)awareness of the agents. We also present a new AUML connector to create protocols that take into account the agent awareness to implement proactive behaviour, and several communication scenarios are proposed to show practical applications of this model.

Credit risk assessment and meta-judgment

Decision-making problems in credit risk assessment and authorization are often too complex to be processed by conventional methods. They involve judgmental procedures which are by nature non-deterministic. Artificial intelligence methods can be used to approach the problem from a different angle. This paper shows the feasibility of a multi-expert approach driven by a meta-model in assessing business risk. More precisely it describes the meta-expert as part of a knowledge-based system being developed to assist bank loan officers in assessing the activities of firms applying for a loan. The use of a meta-model to represent the experts strategic reasoning has the advantage of offering adaptable systems capable of building dynamically the resolution strategy most suited to the problem to be solved.

Contextual activation for Agent-Based Simulation

When designing agent-based simulation, thechoice of a coordination model is a key issue, since oneof the difficulties is to link the activation of the agentswith their context efficiently. Current solutions separatethe activation phase from the action phase of theagents, and each action phase is based on local agentcontext analysis which is time-expensive. Moreover, becausethe link between the context and the action is aninternal part of the agent, it is more difficult to modifythe way the agent reacts to the context without alteringthe way the agent is implemented. Our proposal, calledEASS (Environment as Active Support for Simulation),is a new approach for agent activation, where the contextis analysed inside the environment and conditionsthe activation of the agents. The main result of contextualactivation is to simplify the achievement of complexsimulations and to decrease run-time. The EASS modelhas been implemented within the kernel of MadKit, amulti-agent platform, and the first results are given.

An agent-based computational approach for urban traffic regulation

This paper proposes a bimodal urban traffic control strategy based on a multi-agent model. We call bimodal traffic, a traffic which takes into account both private vehicles and public vehicles such as buses. The objective of this research is to improve global traffic, to reduce bus delays and to improve bus regularity in congested areas of the network. In our agent-based approach, traffic regulation is obtained thanks to communication, collaboration and negotiation between heterogeneous agents. An important feature of our system is that it allows regulation at two levels: macroscopic and microscopic levels. To model in depth regulation procedures, we have introduced special features such as priority levels for buses, computation and update of traffic signal plans, urgency index of intersection stages depending on the level of congestion on the arcs. We have tested our strategy on a small network of six intersections, using the JADE platform. The simulation is described and preliminary results are presented. They show that our MAS strategy improves bus travel time while improving also private vehicles’ travel time, decreases bus delays and improves its regularity compared to a classical strategy called fixed-time control strategy.

A transportation decision support system in agent-based environment

This paper presents a Transportation Decision Support System TDSS called SATIR that reports the network activity in real-time and thus assists the bus network regulators in their various tasks. We propose a global approach to help regulators to manage the bus network under normal conditions network monitoring, dynamic schedule management, with data inconsistencies management and under disrupted conditions disturbance diagnosis and action planning. This global bottom-up approach is based on a multi-agent model where agents encompass the domain knowledge and where the interactions between agents are based on our new model of the environment, the ESAC model Environment as Active Support of Communication. A formal modeling of the disturbance concept, which makes it possible to capitalize the knowledge available within a monitoring station and to follow up the evolution of the disturbances in real time, is presented, followed by a description of its application to a problem of bus network diagnosis in the STIB network Brussels Intercity Transportation Company network. The paper concludes by looking briefly at how using the multi-agent paradigm could help to develop new functionalities to improve Transportation Decision Support Systems design.