Experiential archaeology: Is virtual time travel possible?

We propose an approach for developing efficient search algorithms through genetic programming. Focusing on the game of chess we evolve entire game-tree search algorithms to solve the Mate-In-N problem: find a key move such that even with the best possible counterplays, the opponent cannot avoid being mated in (or before) move N. We show that our evolved search algorithms successfully solve several instances of the Mate-In-N problem, for the hardest ones developing 47% less gametree nodes than CRAFTY--a state-of-the-art chess engine with a ranking of 2614 points. Improvement is thus not over the basic alpha-beta algorithm, but over a world-class program using all standard enhancements.

Evolution of an efficient search algorithm for the mate-in-N problem in chess

In this work, an exponential effective function (EEF) is developed as fitness function applied in a hybrid-Genetic Algorithm (hybrid-GA) to propose a genetic-based effective approach to the glider path-planning of ocean-sampling mission in variable oceans. The proposed EEF is such an objective function that is able to be implemented in optimization algorithm such as Genetic Algorithm (GA) for evaluation of the fittest path. In consideration of the glider path-planning problem (GPP), two motivations are driven by the proposed approach to the glider path-planning in discovery of: (1) a reachable path with the upstream-current avoidance (UCA) in variable oceans; (2) an efficient path for the glider ocean-sampling mission. The exponential combination of the glider motion and current effects as well as the cruising distance benefits the path in terms of reachability and efficiency. The reachability is the first motivation to discover a reachable path implemented by the scheme of UCA, while the efficiency is the second motivation to shorten the cruising distance. Meanwhile, the stabilized path solution is accomplished by hybrid-GA. In variable oceans, currents severely impact the path solution and lead the global optimum to absence. Therefore, alternative is to discover an optimal path with the minimum upstream-current sub-paths to approximate the minimal cruising distance in the condition that the discovered cruising distance should be less than the glider cruising range. To deeply analyze the path reachability, two theorems are developed to verify the conditions of the downstream-current angle (DCA). To evaluate the path-planning performances, 6 state-of-the-art fitness functions are studied and used to make a fair comparison with the EEF in hybrid-GA. First of all, 112 scenarios are created in the restricted random current variations (RRCV). Secondly, 21 scenarios are created in the near-real Kuroshio Current of east Taiwan (KCET) introducing from an ocean prediction model. These scenarios are designed to evaluate fairly the EEF in hybrid-GA. Numeric results show that whether the RRCV or the KCET, the proposed EEF indeed is able to discover the optimal path with the benefits of reachability and efficiency. Therefore, the proposed genetic-based effective approach is well developed to solve the GPP in variable oceans.

A genetic-based effective approach to path-planning of autonomous underwater glider with upstream-current avoidance in variable oceans

To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.

/pdf/evolving-models-from-observed-human-performance-4c1csj1fmc.pdf

Evolving models from observed human performance

The purpose of this research is to provide power grid energy efficiency solutions. In this paper, a comprehensive review and its optimal solution is proposed considering the various challenges of smart grid demand-side management. The main technique is based on a novel idea in the Smart Grid—demand response optimization which enables autonomous energy management on the demand side for a wide variety of customers. The first section of this research examines the smart grid issue and evaluates the state-of-the-art load management techniques in terms of the work’s scope. The demand-side load management architecture consists of three primary levels, two of them in line planning and low-cost scheduling, while the third layer, demand response which is a significant expansion of this domain. The implementation of the proposed architecture in MATLAB/Simulink, with test results. demonstrating the significance of the proposed solution

/pdf/optimal-scheduling-of-demand-side-load-management-of-smart-23dmr8p4.pdf

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Computer game playing is an important artificial intelligence research field in that the results can usually be applied to other related fields. One of the key computer-game-playing issues is designing effective search algorithms. Traditional search algorithms incur great temporal and spatial complexities when exploring deeply into search trees to find good next moves. Searches are thus usually not deep enough to derive good playing strategies. In this paper, we focus on one-player game search trees, and propose a genetic-algorithm-based approach to enhancing the speed and accuracy of game tree searches. Experiments show that our algorithm can improve solution accuracy and search speed.

Applying genetic algorithms to game search trees

In this paper, we consider the problem of finding good next moves in two-player games. Traditional search algorithms, such as minimax and α-β pruning, suffer great temporal and spatial expansion when exploring deeply into search trees to find better next moves. The evolution of genetic algorithms with the ability to find global or near global optima in limited time seems promising, but they are inept at finding compound optima, such as the minimax in a game-search tree. We thus propose a new genetic algorithm-based approach that can find a good next move by reserving the board evaluation values of new offspring in a partial game-search tree. Experiments show that solution accuracy and search speed are greatly improved by our algorithm.

/pdf/adversarial-search-by-evolutionary-computation-8w4kxrc43b.pdf

Adversarial Search by Evolutionary Computation

The authors consider the problem of finding good next moves in two-player games. Traditional search algorithms, such as minimax and /spl alpha/-/spl beta/ pruning suffer great temporal and spatial expansion when exploring deeply into search trees to find better next moves. The evolution of genetic algorithms, abilities to find global or near global optima in limited times seems promising, but they are inept at finding compound optima such as the minimax in a game search tree. They propose a new genetic-algorithm-based approach that can find a good next move by reserving the board evaluation values of new offspring in a partial game-search tree. Experiments show that solution accuracy and search speed are greatly improved by the algorithms.

A genetic minimax game-playing strategy

It is well known that when exploring a game tree, the deeper the depth, the more accurate the move prediction but greater temporal and spatial expansion is required. How to explore the game tree deeper is a great challenge in such research. In T.P. Hong et al (Int. Conf. on Evolutionary Computation, Anchorage, Alaska, USA, p.690-4, 1998), we proposed a genetic algorithm-based search method for two-player games. In this paper, we generalize that method to solve multi-player game-search problems. We propose a genetic algorithm-based approach that can find good next moves in multi-player games without the requirement for great temporal and spatial expansion.

Ke-Yuan Huang

Papers

Applying genetic algorithms to game search trees

Adversarial Search by Evolutionary Computation

A genetic minimax game-playing strategy

A genetic search method for multi-player game playing