Self-interested planning agents using plan repair

We present a novel approach to multiagent planning for selfinterested agents. The main idea behind our approach is that multiagent planning systems should be built upon (singleagent) plan repair systems. In our system agents can exchange goals and subgoals through an auction, using their own heuristics or utility functions to determine when to auction and what to bid. Some experimental results for a logistics domain demonstrate empirically that this system supports the coordination of self-interested agents

Plan Repair in Single-Agent and Multi-Agent Systems

Electrical Engineering, Mathematics and Computer Scienc

Diagnosis of single and multi-agent plans

We discuss the application of Model Based Diagnosis in agent-based planning. We model a plan as a system to be diagnosed and assume that agents can monitor the execution of the plan by making partial observations during plan execution. These observations are used by the agents to explain plan deviations (errors) by qualifying some action instances as behaving abnormally. We prefer those qualifications that are maximum informative, i.e. explain as much as possible. Since in a plan several instances of the same action might occur, an error occurring in one instance might be used to predict the occurrence of the same error in an action instance to be executed later on. To account for such correlations, we introduce causal rules to generate diagnoses from action instances qualified as abnormally and we introduce Pareto minimal causal diagnoses as the right extension of classical minimal diagnoses.Next, we consider the multi-agent perspective where each agent is responsible for a part of the total plan, we show how plan-diagnoses of these partial plans are related to diagnoses of the total plan and how global diagnoses can be obtained in a distributed way

Plan Repair as an Extension of Planning

In dynamic environments, agents have to deal with changing situations. In these cases, repairing a plan is often more efficient than planning from scratch, but existing planning techniques are more advanced than existing plan repair techniques. Therefore, we propose a straightforward method to extend planning techniques such that they are able to repair plans. This is possible, because plan repair consists of two different operations: (i) removing actions, and (ii) adding actions. Adding actions is similar to planning, but planning heuristics can also be used for removing actions, which we call unrefinement. We present a plan repair template that reflects these two operations, and we present a heuristic for unrefinement that uses arbitrary existing planning techniques. We show that the resulting method is much better than replanning from scratch, and also significantly better than another plan repair method (GPG). Furthermore, we show that the plan repair template is a generalisation of existing plan repair methods.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Self-interested Planning Agents using Plan Repair

We present a novel approach to multiagent planning for selfinterested agents. The main idea behind our approach is that multiagent planning systems should be built upon (singleagent) plan repair systems. In our system agents can exchange goals and subgoals through an auction, using their own heuristics or utility functions to determine when to auction and what to bid. Some experimental results for a logistics domain demonstrate empirically that this system supports the coordination of self-interested agents.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc