Improving AI Planning using Map Reduce

Today, the Landmark concept is adapted from the classical planning to work in hierarchical task network planning. It was shown how it is used to extracts landmark literals from a given hierarchical planning domain and problem description and then use these literals to update the the planning domain by ruling out the irrelevant tasks and methods before the actual planning is performed. In this paper, we compine the landmark concept with the Map-reduce framework to increase the performance of the planning process. Our empirical evaluation shows that the combination between landmark and Map-Reduce framework dramatically improves performance of the planning process.

Hierarchical Task Network Planning with Task Insertion and State Constraints

We extend hierarchical task network planning with task insertion (TIHTN) by introducing state constraints, called TIHTNS. We show that just as for TIHTN planning, all solutions of the TIHTNS planning problem can be obtained by acyclic decomposition and task insertion, entailing that its plan-existence problem is decidable without any restriction on decomposition methods. We also prove that the extension by state constraints does not increase the complexity of the plan-existence problem, which stays 2-NEXPTIME-complete, based on an acyclic progression operator. In addition, we show that TIHTNS planning covers not only the original TIHTN planning but also hierarchy-relaxed hierarchical goal network planning.

Modified Adversarial Hierarchical Task Network Planning in Real-Time Strategy Games

Real-time strategy (RTS) game has proposed many challenges for AI research for its large state spaces, enormous branch factors, limited decision time and dynamic adversarial environment. To tackle above problems, the method called Adversarial Hierarchical Task Network planning (AHTN) has been proposed and achieves favorable performance. However, the HTN description it used cannot express complex relationships among tasks and impacts of environment on tasks. Moreover, the AHTN cannot handle task failures during plan execution. In this paper, we propose a modified AHTN planning algorithm named AHTNR. The algorithm introduces three elements essential task, phase and exit condition to extend the HTN description. To deal with possible task failures, the AHTNR first uses the extended HTN description to identify failed tasks. And then a novel task repair strategy is proposed based on historical information to maintain the validity of previous plan. Finally, empirical results are presented for the μRTS game, comparing AHTNR to the state-of-the-art search algorithms for RTS games.