We pose the problem of constructing good search heuristics as an optimization problem: minimizing the loss between the true distances and the heuristic estimates subject to admissibility and consistency constraints. For a well-motivated choice of loss function, we show performing this optimization is tractable. In fact, it corresponds to a recently proposed method for dimensionality reduction. We prove this optimization is guaranteed to produce admissible and consistent heuristics, generalizes and gives insight into differential heuristics, and show experimentally that it produces strong heuristics on problems from three distinct search domains.

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

Much recent work in satisficing planning has aimed at striking a balance between coverage - solving as many problems as possible - and plan quality. Current planners achieve near perfect coverage on the latest IPC benchmarks. It is therefore natural to investigate their scaling behavior on more difficult instances. Among state of the art planners, LAMA (Richter, Helmert, and Westphal 2008) is able to generate high quality plans, but its coverage drops off rapidly with increasing prob- lem complexity. The Arvand planner (Nakhost and Müller 2009) scales to much harder instances but generates lower quality plans. This paper introduces a new algorithm, Monte Carlo Random Walk-based Local Tree Search (MRW-LTS), which uses random walks to selectively build local search trees. Experiments demonstrate that MRW-LTS combines a scaling behavior that is better than LAMA’s with a plan quality that is better than Arvand’s.

This work proposes new approaches to contingent planning using alternative belief state representations extended from those in conformant planning and a new AND/OR forward search algorithm, called PrAO, for contingent solutions. Each representation was implemented in a new contingent planner. The important role of belief state representation has been confirmed by the fact that our planners all outperform other stateof- the-art planners on most benchmarks and the comparison of their performances varies across all the benchmarks even using the same search algorithm PrAO and same unsophisticated heuristic scheme. The work identifies the properties of each representation method that affect the performance.

We present an algorithm for compressing pattern databases (PDBs) and a method for fast random access of these com-pressed PDBs. We demonstrate the effectiveness of our technique by compressing two 6-tile sliding-tile PDBs by a factor of 12 and a 7-tile sliding-tile PDB by a factor of 24.

Real-time heuristic search algorithms that precompute search space-specific databases have demonstrated exceptional performance in video-game pathfinding. We discuss the first steps towards extending these algorithms to other search spaces that also benefit from the real-time property. We present our initial progress in characterizing the performance of current algorithms based on the features of a search space, and discuss future directions of this research.

A major difficulty in a search-based problem-solving process is the task of searching the potentially huge search space resulting from the exponential growth of states. State explosion rapidly occupies memory and increases computation time. Although various heuristic search algorithms have been developed to solve problems in a reasonable time, there is no efficient method to construct heuristic functions. In this work, we propose a method by which a neural network can be iteratively trained to form an efficient heuristic function. An adaptive heuristic search procedure is involved in the training iterations. This procedure reduces the evaluation values of the states that are involved in the currently known best solution paths. By doing so, the promising states are continuously moved forward. The adapted heuristic values are fed back to neural networks; thus, a well-trained network function can find the near-best solutions quickly. To demonstrate this method, we solved the fifteen-puzzle problem. Experimental results showed that the solutions obtained by our method were very close to the shortest path, and both the number of explored nodes and the search time were significantly reduced.

Agents in highly dynamic adversarial domains, such as RTS games, must continually make time-critical decisions to adapt their behaviour to the changing environment. In such a context, the planning agent must consider his opponent's actions as uncontrollable, or at best influenceable. In general nondeterministic domains where there is no clear turn-taking protocol, most heuristic search methods to date do not explicitly reason about the opponent's actions when guiding the state space exploration towards goal or high-reward states. In contrast, we are investigating a domain-independent heuristic planning approach which reasons about the dynamics and uncontrollability of the opponent's behaviours in order to provide better guidance to the search process of the planner. Our planner takes as input the opponent's behaviours recognized by a plan recognition module and uses them to identify opponent's actions that lead to low-utility projected states. We believe such explicit heuristic reasoning about the potential behaviours of the opponent is crucial when planning in adversarial domains, yet is missing in today's planning approaches.

Dots-And-Boxes is a well-known and widely-played combinatorial game. While the rules of play are very simple, the state space for even small games is extremely large, and finding the outcome under optimal play is correspondingly hard. In this paper we introduce a Dots-And-Boxes solver which is significantly faster than the current state-of-the-art: over an order-of-magnitude faster on several large problems. We describe our approach, which uses Alpha-Beta search and applies a number of techniques—both problem-specific and general—to reduce the number of duplicate states explored and reduce the search space to a manageable size. Using these techniques, we have determined for the first time that Dots- And-Boxes on a board of 4x5 boxes is a tie given optimal play. This is the largest game solved to date.

Logic, and declarative representation of knowledge in general, have long been a preferred framework for problem solving in AI. However, specific subareas of AI have been eager to abandon general-purpose knowledge representation in favor of methods that seem to address their computational core problems better. In planning, for example, state-space search has in the last several years been preferred to logic-based methods such as SAT. In our recent work, we have demonstrated that the observed performance differences between SAT and specialized state-space search methods largely go back to the difference between a blind (or at least planning-agnostic) and a planning-specific search method. If SAT search methods are given even simple heuristics which make the search goal-directed, the efficiency differences disappear.