Supporting Assumption-Based Reasoning in a Distributed Environment

Within the pede project we investigate the generation and execution of plans in distributed environments. As our approach is based on multi-agent systems with some or all agents performing assumption-based reasoning, we ran into the distributed belief revision problem. In order to build planning and executing agents that are able to safely perform their tasks in a highly dynamic environment, a mechanism to support the revision of decisions is required. Reason maintenance techniques are a standard approach to solve this problem, but currently available technology for the distributed case has proven inadequate for the pede domain. The new architecture presented in this paper { darms { is a true extension of deKleer's atms to multi-agent scenarios and provides exible support for revisable reasoning in distributed environments. The pede 1 project studies techniques to improve the eecient generation and fail-safe execution of plans in distributed, highly dynamic environments. In a pede domain, we typically have several planning agents (planners) and a potentially large number of executing agents (executers), for instance humans, machines, robots, and autonomous transport devices. The decisions made by planners as well as the actions performed by the executers depend on the planners' knowledge of the world state at planning time (e.g. delivery dates for parts) and on information communicated by other agents (e.g. availability of machines, tools, or parts at a speciic place at a particular time). Furthermore, many planners must make assumptions about future states of the world in order to make planning decisions. If the world state changes in an unexpected way, or an information-providing agent revises its information, or assumptions made at planning time do not hold at execution time, plans will not be safely executable and the executers will run into failure. To ensure safe execution of plans, planners need to revise their decisions as soon as changes to the underlying planning decisions become known and to propagate the eeects to other agents. Without taking special measures, an agent updating information and revising its decisions does not know whether the changes aaect any other agents, what other agents are aaected and how