【正文】 rated (Roozemond, 1998): data collection / distribution (via RSA information on the current state of traffic。 model the world they operate in and plan and predict consequences of actions and evaluate alternatives. The problem solving ponent of an intelligent agent can be a rulebased system but can also be a neural work or a fuzzy expert system. It may be obvious that finding a feasible solution is a necessity for an agent. Often local optima in decentralised systems, are not the global optimum. This problem is not easily solved. The solution has to be found by tailoring the interaction mechanism or to have a supervising agent coordinating the optimisation process of the other agents. Intelligent agents in UTC,a helpful paradigm Agent technology is applicable in different fields within UTC. The ones most important mentioning are: information agents, agents for traffic simulation and traffic control. Currently, most applications of intelligent agents are information agents. They collect information via a work. With special designed agents user specific information can be provided. In urban traffic these intelligent agents are useable in delivering information about weather, traffic jams, public transport, route closures, best routes, etc. to the user via a Personal Travel Assistant. Agent technology can also be used for aggregating data for further distribution. Agents and multi agent systems are capable of simulating plex systems for traffic simulation. These systems often use one agent for every traffic participant (in a similar way as object oriented programs often use objects). The application of agents in (Urban) Traffic Control is the one that has our prime interest. Here we ultimately want to use agents for proactive traffic light control with online optimisation. Signal plans then will be determined based on predicted and measured detector data and will be tuned with adjoining agents. The most promising aspects of agent technology, the flexibility and proactive behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not that flexible, it is unable to adjust itself if situations change and can39。 have goals and intentions。 but almost none of the current available tools behave proactively or have metarules that may change behaviour of the controller incorporated into the system. The next logical step for traffic control is the inclusion of these metarules and pro active and goaloriented behaviour. The key aspects of improved control, for which contributions from artificial intelligence and artificial intelligent agents can be expected, include the capability of dealing with conflicting objectives。the so called adaptive systems. Real adaptive systems will need proactive calculated traffic information and cycle plans based on these calculated traffic conditions to be updated frequently. Our research of the usability of agent technology within traffic control can be split into two parts. First there is a theoretical part integrating agent technology and traffic control. The final stage of this research focuses on practical issues like implementation and performance. Here we present the concepts of agent technology applied to dynamic traffic control. Currently we are designing a layered model of an agent based urban traffic control system. We will elaborate on that in the last chapters. Adaptive urban traffic control Adaptive signal control systems must have a capability to optimise the traffic flow by adjusting the traffic signals based on current traffic. All used traffic signal control methods are based on feedback algorithms using traffic demand data varying from years to a couple of minutes in the past. Current adaptive systems often operate on the basis of adaptive green phases and flexible coordination in (sub)works based on measured traffic conditions (., UTOPIAspot,SCOOT). These methods are still not optimal where traffic demand changes rapidly within a short time interval. The basic premise is that existing signal plan generation tools make rational decisions about signal plans under varying conditions。 understand information。 synthesise new concepts and / or ideas。s. One final aspect to be mentioned is the robustness of agent based systems (if all munication fails the agent runs on, if the agent fails a fixed program can be executed. To be able to keep our first urban traffic control model as simple as possible we have made the following assumptions: we limit ourselves to inner city traffic control (road segments, intersections, corridors), we handle only controlled intersections with detectors (intensity and speed) at all road segments, we only handle cars and we use simple rule bases for knowledge representation. Types of agents in urban intersection con