This article argues that a DAI approach can be used to cope with the complexity of industrial applications. DAI techniques are beginning to have a broad impact; the current introduction of these techniques by an ESPRIT project, a Palo Alto consortium, ARPA, Carnegie Mellon University, MCC, and others are good examples. In the near future, other industrial products will emerge from the application of DAI techniques to other domains, including distributed databases, computer-supported cooperative work, and air traffic control. An important advantage of a DAI approach is the ability to integrate existing standalone knowledge-based systems. This factor is important because software for industrial applications is often developed in an ad hoc fashion. Thus, organizations possess a large number of standalone systems developed at different times by different people using different techniques. These systems all operate in the same physical environment, all have expertise that is related but distinct, and all could benefit from cooperation with other such standalone systems

Computer traffic simulation is important for making new traffic-control strategies. Microscopic traffic simulators can model traffic flow in a realistic manner and are ideal for agent-based vehicle control. In this paper we describe a model of a reactive agent that is used to control a simulated vehicle. The agent is capable of tactical-level driving and has different driving styles. To ensure fast reaction times, the agent's driving task is divided in several competing and reactive behaviour rules. The agent is implemented and tested in a prototype traffic simulator program. The simulator consists of an urban environment with multi-lane roads, intersections, traffic lights, light controllers and vehicles. Every vehicle is controlled by a driving agent and all agents have individual behaviour settings. Preliminary experiments have shown that the agents exhibit human-like behaviour ranging from slow and careful to fast and aggressive driving behaviour.

Computer traffic simulation is important for making new traffic-control strategies. Microscopic traffic simulators can model traffic flow in a realistic manner and are ideal for agent-based vehicle control. In this paper we describe a model of a reactive agent that is used to control a simulated vehicle. The agent is capable of tactical-level driving and has different driving styles. To ensure fast reaction times, the agent's driving task is divided in several competing and reactive behaviour rules. The agent is implemented and tested in a prototype traffic simulator. The simulator consists of an urban environment with multi-lane roads, intersections, traffic lights, and vehicles. Every vehicle is controlled by a separate driving agent and all agents have individual behaviour settings. Preliminary experiments have shown that the agents exhibit human-like behaviour ranging from slow and careful to fast and aggressive driving behaviour.

Computer traffic simulation is important for making new traffic-control strategies. Microscopic traffic simulators can model traffic flow in a realistic manner and are ideal for agent-based vehicle control. In this paper we describe a model of a reactive agent that is used to control a simulated vehicle. The agent is capable of tactical-level driving and has different driving styles. To ensure fast reaction times, the agent's driving task is divided in several competing and reactive behaviour rules. The agent is implemented and tested in a prototype traffic simulator. The simulator consists of an urban environment with multilane roads, intersections, traffic lights, and vehicles. Every vehicle is controlled by a separate driving agent and all agents have individual behaviour settings. Preliminary experiments have shown that the agents exhibit human-like behaviour ranging from slow and careful to fast and aggressive driving behaviour.

Computer traffic simulation is important for making new traffic-control strategies. Microscopic traffic simulators can model traffic flow in a realistic manner and are ideal for agent-based vehicle control. In this paper we describe a model of a reactive agent that is used to control a simulated vehicle. The agent is capable of tactical-level driving and has different driving styles.