Abstracting Knowledge from Annotated Chinese-Chess Game Records

Abstract

Expert knowledge is crucial for improving the strength of computer Chinese-Chess programs. Although a great deal of expert knowledge is available in text format that using natural languages, manually transforming it into computer readable forms is time consuming and difficult. Written expert annotations of Chinese-Chess games show different styles. By analyzing and collecting commonly used phrases and patterns from experts' annotations, we introduce a novel pattern matching strategy. It automatically epitomises knowledge from a large number of annotated game records. The results of the experiments on the analysis of the middle phase of games indicate that our strategy achieves a low error rate. We hope to exploit this approach to collect automatically a great diversity of Chinese-Chess knowledge that is currently in text format. 1

Mining Experiential Patterns from Game-Logs of Board Game

“…The ID of pieces-state during the process of data extraction (see lines (17) and (23) in Algorithm 1).…”

“…Discussion on the Using of Game-Logs. Previous works [17][18][19][20][21][22][23][24] mainly discuss the methods of extracting expert knowledge from game records. The extracted knowledge is commonly used to construct a game agent of high intelligence.…”

“…Recently, researchers find that game-logs (game records) cover a lot of important and interesting knowledge. Chen et al [17] proposed an approach to abstract expert knowledge from annotated Chinese chess game records. GO game records are used for the life and death predicting, the pattern acquisition, and the pattern matching [18][19][20].…”

“…(15) for 𝑚 = 1 to stateIds.length − 1 do(16) if node.value == 0 then(17) node.value = |𝑠𝑡𝑎𝑡𝑒𝐼𝑑𝑠[0]|; (18) endif (19) isFound = false; (20) if node.branches != null then (21) for 𝑛 = 0 to node.branches.length − 1 do (22) if node.branches[𝑛].next.value == |𝑠𝑡𝑎𝑡𝑒𝐼𝑑𝑠[𝑚]| then (23) node.branches[𝑛].weight += (stateIds[𝑚]/|𝑠𝑡𝑎𝑡𝑒𝐼𝑑𝑠[𝑚]|); (24) node = node.branches[𝑛].next; next = new Node(value = |𝑠𝑡𝑎𝑡𝑒𝐼𝑑𝑠[𝑚]|, branches = null)…”

Integration of Chinese Chess Endgame Knowledge Bases

“…In the middle game, people often use a nega-scout algorithm with a good evaluation function and a nice move ordering scheme to obtain a good solution [2]. There are also strategies in artificial intelligence that automatically generate middle game evaluation functions [3]. In the endgame phase, the performance of today's CHINESE CHESS program is still not satisfiable compared to human experts.…”