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.
3900405
Game-SAT: A Preliminary Report
Game-SAT is a 2-player version of SAT where two players (MAX and MIN) play on a SAT instance by alternatively selecting a variable and assigning it a value true or false. MAX tries to make the formula true, while MIN tries to make it false. The Game-SAT problem is to determine the winner of a SAT instance under the rules above, assuming the perfect play by both players. The Game-SAT problem, originally derived from an application in adversarial planning, is PSPACE-complete. The problem is similar to QBF, but differs by the property of free variable selection, compared to QBF with its fixed variable ordering. We have developed a Game-SAT solver, Gasaso, that uses a combination of standard game tree search techniques, search methods that are well known in the SAT community, and specialized pruning techniques. We show empirically how the solver performs in this new domain, and give evidence for the existence of phase transitions in this problem.
18731885
A solution to the GHI problem for depth-first proof-number search
The Graph-History interaction (GHI) problem is a notorious problem that causes game-playing programs to occasionally return incorrect solutions. This paper presents a practical method to cure the GHI problem for the case of the df-pn algorithm. Results in the game of Go with the situational super-ko rule show that the overhead incurred by our method is very small, while correctness is always guaranteed.
13487020
Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement
Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the best known plan divided by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan by creating a plan neighborhood graph PNG(π) of a given plan π, and then extracts a shortest path from PNG(π). Both methods are implemented in the ARAS postprocessor and are empirically shown to improve the result of several planners, including the top four planners from IPC-2008, under competition conditions.
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].
2893516
Fast Planning with Iterative Macros
Research on macro-operators has a long history in planning and other search applications. There has been a revival of interest in this topic, leading to systems that successfully combine macrooperators with current state-of-the-art planning approaches based on heuristic search. However, research is still necessary to make macros become a standard, widely-used enhancement of search algorithms. This article introduces sequences of macro-actions, called iterative macros. Iterative macros exhibit both the potential advantages (e.g., travel fast towards goal) and the potential limitations (e.g., utility problem) of classical macros, only on a much larger scale. A family of techniques are introduced to balance this trade-off in favor of faster planning. Experiments on a collection of planning benchmarks show that, when compared to low-level search and even to search with classical macro-operators, iterative macros can achieve an impressive speed-up in search.
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.
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.
39962861
Weblogs, Legitimacy and Organizations
The last decades have seen a movement of production to less economically developed countries. These countries often don’t meet Western social and environmental standards. Non-governmental organizations (NGOs) have challenged poor conditions such as child labor, discrimination or environmental pollution caused by suppliers. Purchasers are criticized in the public realm, which leads to damage to their reputation [Hansen and Schrader 2005; Gilbert 2003]. Nike, Dole Foods and GM have experienced such campaigns [Lawrence 2002]. Focal companies with direct contact to the end customer and big labels are most vulnerable to this strategy. A loss of reputation may weaken their ability to compete and result in decreasing sales volume [Hansen and Schrader 2005].