Adaptive planning and scheduling in dynamic task domains

There are many application domains such as flexible manufacturing systems (FMS) in which the world changes during the problem solving process or about which the problem solver does not have complete information a priori. A problem solver in such environments is required to take advantage of up-to-date information that becomes available, on line, and to use this information in order to avoid producing solutions that are obsolete by the time they are to be executed. In this paper, we propose an algorithm which performs problem solving on line in order to obtain new information about the availability of resources in its local surroundings. The algorithm performs partial planning followed by partial execution, in order to take immediate advantage of resources which become available and remain available for a short period of time. As part of this paper, we introduce a model of dynamicity included in a graph representation of a task. The decision on the time points at which the resource availability information is probed and updated is automatically made according to the parameters of the model of dynamicity in the environment. We provide theoretical and empirical analyses of the proposed algorithm for a routing problem in the proposed dynamic model. A set of candidate algorithms were chosen for the performance-comparison experiments each of which is suitable for a particular condition (i.e. some produce good results in a static environment and some produce better results in a highly dynamic environment). Results of our performance-comparison experiments show that the proposed algorithm performs as well as the best of the candidate algorithms under a wide range of experiment parameters. The results also show that the proposed algorithm is capable of automatically adapting to the degree of dynamicity in the environment