Showing papers on "Multi-agent planning published in 1997"
1 Jan 1997
TL;DR: This framework can form a basis for mixed initiative user/system agents working together to mutually constraSn task descriptions and plans and to coordinate the task-oriented generation, refinement and ena~ztmeat of those plans.
Abstract: Work is described which seeks to support multiagent mixed initiative interaction between a "task assignment" or "command" agent and a planning agent1. Each agent maintains an agenda of outstanding tasks it is engaged in and uses a common representation of tasks, plans, processes and activities based on the notion that these are all "constraints on behaviour’. Interaction between the agents uses explicit task and option management information. This framework can form a basis for mixed initiative user/system agents working together to mutually constraSn task descriptions and plans and to coordinate the task-oriented generation, refinement and ena~ztmeat of those plans.
1 citations
TL;DR: An approach to multi‐agent planning that contains heuristic elements that reduces overall planning time and derives a plan that approximates the optimal global plan that would have been derived by a central planner, given those original subgoals.
Abstract: The subject of multidagent planning has been of continuing concern in Distributed Artificial Intelligence (DAI). In this paper, we suggest an approach to multidagent planning that contains heuristic elements. Our method makes use of subgoals, and derived subdplans, to construct a global plan. Agents solve their individual subdplans, which are then merged into a global plan. The suggested approach reduces overall planning time and derives a plan that approximates the optimal global plan that would have been derived by a central planner, given those original subgoals. We explore three different scenarios. The first involves a group of agents with a common goal. The second considers how agents can interleave planning and execution when planning towards a common, though dynamic, goal. The third examines the case where agents, each with their own goal, can plan together to reach a state in consensus for the group. Finally, we consider how these approaches can be adapted to handle rational, manipulative agents.