Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Go: an AI oriented survey
Artificial Intelligence
Fuzzy logic-based expert system to predict the results of finite element analysis
Knowledge-Based Systems
Using Ontologies and Vocabularies for Dynamic Linking
IEEE Internet Computing
Developing a group decision support system based on fuzzy information axiom
Knowledge-Based Systems
Interoperable and adaptive fuzzy services for ambient intelligence applications
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A new TOPSIS-based multi-criteria approach to personnel selection
Expert Systems with Applications: An International Journal
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation
IEEE Transactions on Fuzzy Systems
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Diet assessment based on type-2 fuzzy ontology and fuzzy markupl language
International Journal of Intelligent Systems - New Trends for Ontology-Based Knowledge Discovery
An extended TOPSIS for determining weights of decision makers with interval numbers
Knowledge-Based Systems
Society briefs: the game of go @ IEEE WCCI 2010
IEEE Computational Intelligence Magazine
Intelligent agents for the game of go
IEEE Computational Intelligence Magazine
Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)
Knowledge-Based Systems
Revisiting Monte-Carlo tree search on a normal form game: NoGo
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Genetic-based fuzzy image filter and its application to image processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy ontology and its application to news summarization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fuzzy Expert System for Diabetes Decision Support Application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Multicriteria Approach to Data Summarization Using Concept Ontologies
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
NoGo is similar to the game of Go in terms of gameplay; however, the goal is different: the first player who either suicides or kills a group loses the game and the first player with no legal move loses the game. In this paper, we propose an approach combining the technologies of ontologies, evolutionary computation, fuzzy logic, and fuzzy markup language (FML) with a genetic algorithm (GA)-based system for the NoGo game. Based on the collected patterns and the pre-constructed fuzzy NoGo ontology, the genetic FML (GFML) with the fuzzy inference mechanism is able to analyze the situation of the current game board and then play next move to an inferred good-move position. Additionally, the genetic learning mechanism continuously evolves the adopted GFMLs to enable an increase in the winning rate of the GA-based NoGo via playing with the baseline NoGo. In the proposed approach, first, the domain experts construct the important NoGo patterns and the fuzzy NoGo ontology based on the rules of NoGo and the past game records. Second, each GA-based NoGo as White plays against the baseline NoGo as Black according to the inferred and calculated good-move position, respectively. Third, the genetic learning mechanism is carried out to generate two new evolved GFMLs and then the worst two GFMLs stored in the GFML repository are replaced. Fourth, the GFML with the highest winning rate is randomly sampled from the GFML repository in the time series. Finally, one by one the GA-based NoGo adopts the sampled GFML to play lots of games against the baseline NoGo to obtain the winning rate of the GA-based NoGo. The acquired winning rates at the time series show that the proposed approach can work effectively and that the average winning rate of the GA-based NoGo program is much stronger than the baseline NoGo program.