Spatial-temporal reasoning applications of computational intelligence in the game of Go and computer networks

Spatial-temporal reasoning is the ability to reason with spatial images or information about space over time. In this dissertation, computational intelligence techniques are applied to computer Go and computer network applications. Among four experiments, the first three are related to the game of Go, and the last one concerns the routing problem in computer networks. The first experiment represents the first training of a modified cellular simultaneous recurrent network (CSRN) trained with cellular particle swarm optimization (PSO). Another contribution is the development of a comprehensive theoretical study of a 2x2 Go research platform with a certified 5 dan Go expert. The proposed architecture successfully trains a 2x2 game tree. The contribution of the second experiment is the development of a computational intelligence algorithm calledcollective cooperative learning (CCL). CCL learns the group size of Go stones on a Go board with zero knowledge by communicating only with the immediate neighbors. An analysis determines the lower bound of a design parameter that guarantees a solution. The contribution of the third experiment is the proposal of a unified system architecture for a Go robot. A prototype Go robot is implemented for the first time in the literature. The last experiment tackles a disruption-tolerant routing problem for a network suffering from link disruption. This experiment represents the first time that the disruption-tolerant routing problem has been formulated with a Markov Decision Process. In addition, the packet delivery rate has been improved under a range of link disruption levels via a reinforcement learning approach --Abstract, page iv

Ising Graphical Model

The Ising model is an important model in statistical physics, with over 10,000 papers published on the topic. This model assumes binary variables and only local pairwise interactions between neighbouring nodes. Inference for the general Ising model is NP-hard; this includes tasks such as calculating the partition function, finding a lowest-energy (ground) state and computing marginal probabilities. Past approaches have proceeded by working with classes of tractable Ising models, such as Ising models defined on a planar graph. For such models, the partition function and ground state can be computed exactly in polynomial time by establishing a correspondence with perfect matchings in a related graph. In this thesis we continue this line of research. In particular we simplify previous inference algorithms for the planar Ising model. The key to our construction is the complementary correspondence between graph cuts of the model graph and perfect matchings of its expanded dual. We show that our exact algorithms are effective and efficient on a number of real-world machine learning problems. We also investigate heuristic methods for approximating ground states of non-planar Ising models. We show that in this setting our approximative algorithms are superior than current state-of-the-art methods

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 [1], determines the unconditional safety of stones and territories. Müller [3] develops static rules for detecting safety by alternating play, and introduces search-based methods. This paper describes new, stronger search-based techniques including regionmerging 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].