A General Solution to the Graph History Interaction Problem
Since the state space of most games is a directed graph, many game-playing systems detect repeated positions with a transposition table. This approach can reduce search effort by a large margin. However, it suffers from the so-called Graph History Interaction (GHI) problem, which causes errors in games containing repeated positions. This paper presents a practical solution to the GHI problem that combines and extends previous techniques. Because our scheme is general, it is applicable to different game tree search algorithms and to different domains. As demonstrated with the two algorithms αβ and df-pn in the two games checkers and Go, our scheme incurs only a very small overhead, while guaranteeing the correctness of solutions.

title: Sample-based learning and search with permanent and transient memories
abstract: We present a reinforcement learning architecture, Dyna-2, that encompasses both sample-based learning and sample-based search, and that generalises across states during both learning and search. We apply Dyna-2 to high performance Computer Go. In this domain the most successful planning methods are based on sample-based search algorithms, such as UCT, in which states are treated individually, and the most successful learning methods are based on temporal-difference learning algorithms, such as Sarsa, in which linear function approximation is used. In both cases, an estimate of the value function is formed, but in the first case it is transient, computed and then discarded after each move, whereas in the second case it is more permanent, slowly accumulating over many moves and games. The idea of Dyna-2 is for the transient planning memory and the permanent learning memory to remain separate, but for both to be based on linear function approximation and both to be updated by Sarsa. To apply Dyna-2 to 9x9 Computer Go, we use a million binary features in the function approximator, based on templates matching small fragments of the board. Using only the transient memory, Dyna-2 performed at least as well as UCT. Using both memories combined, it significantly outperformed UCT. Our program based on Dyna-2 achieved a higher rating on the Computer Go Online Server than any handcrafted or traditional search based program.

title: An Improved Safety Solver for Computer Go
abstract: 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 , determines the unconditional safety of stones and completely surrounded territories.  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 .

title: Lambda Depth-First Proof Number Search and Its Application to Go
abstract: 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.

title: Fast Planning with Iterative Macros
abstract: 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.

title: Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement
abstract: 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.

title: Weblogs, Legitimacy and Organizations
abstract: 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].

title: TabVer: A Case Study in Table Verbalization
abstract: 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.

title: PAN - The Prolog Analyzer