31682567
Automated Modular Termination Proofs for Real Prolog Programs
We present a methodology for checking the termination of Prolog programs that can be automated and is scalable. Furthermore, the proposed method can be used to locate errors. It has been successfully implemented as part of a tool that uses static analysis based on formal methods in order to validate Prolog programs. This tool is aimed at supporting the design and maintenance of Prolog programs.
11516969
PAN - The Prolog Analyzer
34011032
TabVer: A Case Study in Table Verbalization
The purpose of this paper is to give a brief overview of TabVer, a front-end component that verbalizes large tables generated by a commercial expert system.
658996
Solving Probabilistic Combinatorial Games
Probabilistic combinatorial games (PCGs) are a model for Go-like games recently introduced by Ken Chen. They differ from normal combinatorial games since terminal positions in each subgame are evaluated by a probability distribution. The distribution expresses the uncertainty in the local evaluation. This paper focuses on the analysis and solution methods for a special case, 1-level binary PCGs. Monte-Carlo analysis is used for move ordering in an exact solver that can compute the winning probability of a PCG efficiently. Monte-Carlo interior evaluation is used in a heuristic player. Experimental results show that both types of Monte-Carlo methods work very well in this problem.
206737627
An Improved Safety Solver in Go Using Partial Regions
Previous safety-of-territory solvers for the game of Go have worked on whole regions surrounded by stones of one color. Their applicability is limited to small to medium-size regions. We describe a new technique that is able to prove that parts of large regions are safe. By using pairs of dividing points, even huge regions can be divided into smaller partial regions that can be proven much easier and faster. Our experimental results show that the new technique significantly improves the performance of our previous state of the art safety-of-territory solver. Especially in earlier game phases, the solver utilizing the new technique outperforms the previous solver by a large margin.
14824355
Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators
Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. 
 
We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.
1968971
DF-PN in Go: An Application to the One-Eye Problem
Search algorithms based on the notion of proof and disproof numbers have been shown to be effective in many games. In this paper, we modify the depth-first proof-number search algorithm df-pn, in order to apply it to the game of Go. We develop a solver for one-eye problems, a special case of enclosed tsume-Go [life and death] problems. Our results show that this approach is very promising.
5964589
Learning Partial-Order Macros from Solutions
Despite recent progress in AI planning, many problems remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present an automated method that learns relevant information from previous experience in a domain and uses it to solve new problem instances. Our approach produces a small set of useful macro-operators as a result of a training process. For each training problem, a structure called a solution graph is built based on the problem solution. Macro-operators with partial ordering of moves are extracted from the solution graph. A filtering and ranking procedure selects the most useful macro-operators, which will be used in future searches. We introduce a heuristic technique that uses only the most promising instantiations of a selected macro for node expansion. Our results indicate an impressive reduction of the search effort in complex domains where structure information can be inferred.