scispace - formally typeset
Search or ask a question
Book ChapterDOI

Improving Heuristics On-the-fly for Effective Search in Plan Space

21 Sep 2015-pp 302-308
TL;DR: This work extends single-step-error adaptation to heuristic functions from Partial Order Causal Link (POCL) planning to allow a partial order planner to observe the effective average- step-error during search.
Abstract: The design of domain independent heuristic functions often brings up experimental evidence that different heuristics perform well in different domains. A promising approach is to monitor and reduce the error associated with a given heuristic function even as the planner solves a problem. We extend this single-step-error adaptation to heuristic functions from Partial Order Causal Link (POCL) planning. The goal is to allow a partial order planner to observe the effective average-step-error during search. The preliminary evaluation shows that our approach improves the informativeness of the state-of-the-art heuristics. Our planner solves more problems by using the improved heuristics as compared to when it uses current heuristics in the selected domains.
Citations
More filters
Posted Content
TL;DR: An online error minimization approach in POCL framework is discussed to minimize the step-error associated with the offline learned models thus enhancing their informativeness and scale up the performance of the planner over standard benchmarks, specially for larger problems.
Abstract: In recent years, the planning community has observed that techniques for learning heuristic functions have yielded improvements in performance. One approach is to use offline learning to learn predictive models from existing heuristics in a domain dependent manner. These learned models are deployed as new heuristic functions. The learned models can in turn be tuned online using a domain independent error correction approach to further enhance their informativeness. The online tuning approach is domain independent but instance specific, and contributes to improved performance for individual instances as planning proceeds. Consequently it is more effective in larger problems. In this paper, we mention two approaches applicable in Partial Order Causal Link (POCL) Planning that is also known as Plan Space Planning. First, we endeavor to enhance the performance of a POCL planner by giving an algorithm for supervised learning. Second, we then discuss an online error minimization approach in POCL framework to minimize the step-error associated with the offline learned models thus enhancing their informativeness. Our evaluation shows that the learning approaches scale up the performance of the planner over standard benchmarks, specially for larger problems.

Cites methods from "Improving Heuristics On-the-fly for..."

  • ...The online heuristic adjustment approach is inspired from a recent work presented as technical communication (Shekhar and Khemani 2015) tested in a few planning domains....

    [...]

References
More filters
Proceedings ArticleDOI
14 Apr 2000
TL;DR: A new admissible heuristic for planning is formulated, used to guide an IDA* search, and empirically evaluate the resulting optimal planner over a number of domains.
Abstract: HSP and HSPr are two recent planners that search the state-space using an heuristic function extracted from Strips encodings. HSP does a forward search from the initial state recomputing the heuristic in every state, while HSPr does a regression search from the goal computing a suitable representation of the heuristic only once. Both planners have shown good performance, often producing solutions that are competitive in time and number of actions with the solutions found by Graphplan and SAT planners. HSP and HSPr. however, are not optimal planners. This is because the heuristic function is not admissible and the search algorithms are not optimal. In this paper we address this problem. We formulate a new admissible heuristic for planning, use it to guide an IDA* search, and empirically evaluate the resulting optimal planner over a number of domains. The main contribution is the idea underlying the heuristic that yields not one but a whole family of polynomial and admissible heuristics that trade accuracy for efficiency. The formulation is general and sheds some light on the heuristics used in HSP and Graphplan, and their relation. It exploits the factored (Strips) representation of planning problems, mapping shortest-path problems in state-space into suitably defined shortest-path problems in atom-space. The formulation applies with little variation to sequential and parallel planning, and problems with different action costs.

370 citations

Journal ArticleDOI
TL;DR: The algorithmic techniques used in FF in comparison to hsp are described and their benefits in terms of run-time and solution-length behavior are evaluated.
Abstract: Fast-forward (FF) was the most successful automatic planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS '00) planning systems competition. Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to hsp and evaluates their benefits in terms of run-time and solution-length behavior.

274 citations

Proceedings Article
27 Jul 1997
TL;DR: A variation of Korf's Learning Real Time A* algorithm together with a suitable heuristic function is developed by looking at planning as real time search and the resulting algorithm interleaves lookahead with execution and never builds a plan.
Abstract: The ability to plan and react in dynamic environments is central to intelligent behavior yet few algorithms have managed to combine fast planning with a robust execution. In this paper we develop one such algorithm by looking at planning as real time search. For that we develop a variation of Korf's Learning Real Time A* algorithm together with a suitable heuristic function. The resulting algorithm interleaves lookahead with execution and never builds a plan. It is an action selection mechanism that decides at each time point what to do next. Yet it solves hard planning problems faster than any domain independent planning algorithm known to us, including the powerful SAT planner recently introduced by Kautz and Selman. It also works in the presence of perturbations and noise, and can be given a fixed time window to operate. We illustrate each of these features by running the algorithm on a number of benchmark problems.

261 citations

Journal ArticleDOI
TL;DR: VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP that draws from the experience gained in the early to mid 1990's on flaw selection strategies, and combines this with more recent developments in the field of domain independent planning.
Abstract: VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. It draws from the experience gained in the early to mid 1990's on flaw selection strategies for POCL planning, and combines this with more recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis. We present an adaptation of the additive heuristic for plan space planning, and modify it to account for possible reuse of existing actions in a plan. We also propose a large set of novel flaw selection strategies, and show how these can help us solve more problems than previously possible by POCL planners. VHPOP also supports planning with durative actions by incorporating standard techniques for temporal constraint reasoning. We demonstrate that the same heuristic techniques used to boost the performance of classical POCL planning can be effective in domains with durative actions as well. The result is a versatile heuristic POCL planner competitive with established CSP-based and heuristic state space planners.

178 citations

Proceedings Article
04 Aug 2001
TL;DR: This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms.
Abstract: This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners–viz., distance based heuristics, reachability analysis and disjunctive constraint handling–can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP. Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several “parallel” domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility.

172 citations