Plan Repair in Single-Agent and Multi-Agent Systems

Electrical Engineering, Mathematics and Computer Scienc

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

Plan Repair: A framework and a new heuristic with applications to logistics

Planning can be a valuable tool for supporting a wide array of real-world problems, such as logistics, manufacturing and control. However, these applications are often highly dynamic, resulting in plans that require updating. In such situations, plan repair methods can be used to adapt the plan. In this paper, we propose a general framework for plan repair. This framework is based on an existing general framework for planning, the so-called refinement planning approach. One of the advantages of a general framework is that it helps to understand existing techniques and improve upon them. As an example of this, we show how we can extend an existing planning method into a system that can also deal with plan repair problems. This system is tested on a number of benchmark problems that deal with abstract transportation problems.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Plan Repair using a Plan Library

Plan library's have proven their added value to the efficiency of planning. In this paper, we present results on the use of a plan library to plan repair. We show that using a relatively simple library, we can already obtain significant improvements in efficiency compared to plan repair without a library.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Inefficiencies in Task Allocation for Multiagent Planning with Bilateral Deals

Distributed planning in a multiagent environment may give rise to inefficiencies. We study this effect focussing on the task allocation problem. We show that in the worst case, the result of a multiagent approach can be arbitrarily bad in theory when recontracting and multilateral deals are not allowed. This is a more precise result than was previously known, which was that we are not guaranteed to find the optimal solution. We show that the sources of this disappointing result are the impossibility to come back on (bad) contracts in combination with either selfish agents, or agents that have incomplete information on potential costs. Furthermore, we show some preliminary experimental results of the effect of these causes on the optimality of a solution for multiagent task allocation. Interestingly, none of the experiments exhibit the very negative outcomes that are predicted by the theory. Although it is too early to draw conclusions, this might indicate that in practical situations, the circumstances that lead to the theoretical results are very unlikely.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Resource Based Multi Agent Plan Merging: Framework and application

We discuss a resource-based planning framework where agents are able to merge plans by exchanging resources. In this framework, plans are specified as structured objects composed of resource consuming and resource producing processes (actions). A plan itself can also be conceived as a process consuming input resources and producing output resources. A plan can be improved if we can remove actions from it while maintaining goal realizability.We describe a reduction property that specifies how one agent can improve its plan by using (free) resources from another agent in such a way that goal realizability is preserved. The plan-merging algorithm we use to specify plan merging in a multi-agent context is an iterative, distributed, any-time application of this reduction property. The performance of this algorithm has been evaluated using a planning data set obtained from a taxi company. The quality of the algorithm is measured by the decrease of the total distance driven by all taxis. By allowing passengers to share rides, we create a trade-off between the additional travel time of passengers and the total drive distance. Allowing passengers to be a few minutes later at their destination and share rides, a significant improvement of the plans can be obtained (from 5% up to 30% reduction of the taxi driving distance).Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc