Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Machine Learning
An Artificially Intelligent Sports Tipper
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
A stochastic CAI model for assisting in the design of football strategy
ACM SIGSIM Simulation Digest
Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning
Cybernetics and Systems Analysis
Predicting football results using Bayesian nets and other machine learning techniques
Knowledge-Based Systems
Corporate evidential decision making in performance prediction domains
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Scenario analysis using Bayesian networks: A case study in energy sector
Knowledge-Based Systems
Hybrid integration of reasoning techniques in suspect investigation
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Confidence-based reasoning with local temporal formal contexts
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A strategy in sports betting with the nearest neighbours search and genetic algorithms
Annales UMCS, Informatica
pi-football: A Bayesian network model for forecasting Association Football match outcomes
Knowledge-Based Systems
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We propose a framework for sports prediction using Bayesian inference and rule-based reasoning, together with an in-game time-series approach. The framework is novel in three ways. The framework consists of two major components: a rule-based reasoner and a Bayesian network component. The two different approaches cooperate in predicting the results of sports matches. It is motivated by the observation that sports matches are highly stochastic, but at the same time, the strategies of a team can be approximated by crisp logic rules. Furthermore, because of the rule-based component, our framework can give reasonably good predictions even when statistical data is scanty: it can be used to predict results of matches between teams which have had few previous encounters. Machine learning techniques have great difficulty in handling such situations of insufficient data. Second, our framework is able to consider many factors, such as current scores, morale, fatigue, skills, etc. when it predicts the results of sports matches: most previous work considered only one factor, usually the score. Third, in contrast to most previous work on sports results prediction, we use a knowledge-based in-game time-series approach to predict sports matches. This approach enables our framework to reflect the tides/flows of a sports match, making our predictions certainly more realistic, and somewhat more accurate. We have implemented a football results predictor called FRES (Football Result Expert System) based on this framework, and show that it gives reasonable and stable predictions.