Decision-making and strategic thinking through analogies

When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? Consider the chess game: how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve

Adaptive Parallel Iterative Deepening Search

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

The emergence of choice: Decision-making and strategic thinking through analogies

Consider the chess game: When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve