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.
6160604
Lambda Depth-First Proof Number Search and Its Application to Go
Thomsen's λ search and Nagai's depth-first proof-number (DFPN) search are two powerful but very different AND/OR tree search algorithms. Lambda Depth-First Proof Number search (LDFPN) is a novel algorithm that combines ideas from both algorithms. λ search can dramatically reduce a search space by finding different levels of threat sequences. DFPN employs the notion of proof and disproof numbers to expand nodes expected to be easiest to prove or disprove. The method was shown to be effective for many games. Integrating λ order with proof and disproof numbers enables LDFPN to select moves more effectively, while preserving the efficiency of DFPN. LDFPN has been implemented for capturing problems in Go and is shown to be more efficient than DFPN and more robust than an algorithm based on classical λ search.
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.
18432159
Proof-Set Search
Victor Allis’ proof-number search is a powerful best-first tree search method which can solve games by repeatedly expanding a most-proving node in the game tree. A well-known problem of proof-number search is that it does not account for the effect of transpositions. If the search builds a directed acyclic graph instead of a tree, the same node can be counted more than once, leading to incorrect proof and disproof numbers. While there are exact methods for computing proof numbers in DAGs, they are too slow to be practical.
16970275
An Improved Safety Solver for Computer Go
Most Go-playing programs use a combination of search and heuristics based on an influence function to determine whether territories are safe. However, to assure the correct evaluation of Go positions, the safety of stones and territories must be proved by an exact method. 
 
The first exact algorithm, due to Benson [1], determines the unconditional safety of stones and completely surrounded territories. Muller [3] develops static rules for detecting safety by alternating play, and introduces search-based methods. 
 
This paper describes new, stronger search-based techniques including region-merging and a new method for efficiently solving weakly dependent regions. In a typical final position, more than half the points on the board can be proved safe by our current solver. This almost doubles the number of proven points compared to the 26.4% reported in [3].
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.