Natural Computation in Finance
... THEN stop moving and ambush (enter the ambush state) at the corner or cross point near the nearest power pill waiting for a ghost to come closer, where distance (nearest_power_pill) is the distance from Ms. Pac-Man to the nearest power pill, distance(nearest_ghost) the distance from Ms. Pac-Man to t ...
... THEN stop moving and ambush (enter the ambush state) at the corner or cross point near the nearest power pill waiting for a ghost to come closer, where distance (nearest_power_pill) is the distance from Ms. Pac-Man to the nearest power pill, distance(nearest_ghost) the distance from Ms. Pac-Man to t ...
Monte-Carlo Tree Search for the Physical Travelling Salesman
... can be used to modulate how far the point-mass is allowed to go from the centroid. In this study the value is set to 1.05. We define the following new algorithms using the C ENTROID H EURISTIC: the Centroid Heuristic MC is identical to the Heuristic MC but the heuristic used to guide the MC simulati ...
... can be used to modulate how far the point-mass is allowed to go from the centroid. In this study the value is set to 1.05. We define the following new algorithms using the C ENTROID H EURISTIC: the Centroid Heuristic MC is identical to the Heuristic MC but the heuristic used to guide the MC simulati ...
Tabu Search
... local optima (where all the neighbouring solutions are nonimproving) by guiding a steepest descent local search (or steepest ascent hill climbing) algorithm ...
... local optima (where all the neighbouring solutions are nonimproving) by guiding a steepest descent local search (or steepest ascent hill climbing) algorithm ...
Best Keyword Cover Search
... In keyword-NNE algorithm, the best-first browsing strategy is applied like BF-baseline but large memory requirement is avoided. For the better explanation, we can imagine all candidate keyword covers generated in BF-baseline algorithm are grouped into independent groups. Each group is associated wit ...
... In keyword-NNE algorithm, the best-first browsing strategy is applied like BF-baseline but large memory requirement is avoided. For the better explanation, we can imagine all candidate keyword covers generated in BF-baseline algorithm are grouped into independent groups. Each group is associated wit ...
Strong Cyclic Planning with Incomplete Information and Sensing
... A plan is a solution of this planning problem and can be represented as a graph, in which nodes are labeled with belief states b, edges are labeled with actions executed in the outgoing belief state a(b), and every node b has successor nodes corresponding to boa for every o ∈ O(a, b), that are defin ...
... A plan is a solution of this planning problem and can be represented as a graph, in which nodes are labeled with belief states b, edges are labeled with actions executed in the outgoing belief state a(b), and every node b has successor nodes corresponding to boa for every o ∈ O(a, b), that are defin ...
Cooperative Games with Monte Carlo Tree Search
... Monte Carlo Tree Search (MCTS) is thriving on zerosum games like the Go board game and there are many MCTS extensions that further optimize the selection strategy and improve the performance of the algorithm, e.g. RAVE [5]. In this paper, we are proposing to use MCTS to solve cooperative games and u ...
... Monte Carlo Tree Search (MCTS) is thriving on zerosum games like the Go board game and there are many MCTS extensions that further optimize the selection strategy and improve the performance of the algorithm, e.g. RAVE [5]. In this paper, we are proposing to use MCTS to solve cooperative games and u ...
Case Adaptation by Segment Replanning for Case
... This paper introduces a domain-independent and anytime behavior adaptation process, called CASER (Case Adaptation by Segment Replanning), that finds an easyto-generate solution with low quality and adapts this solution to improve it. It follows the same idea as PbR. However, this method does not req ...
... This paper introduces a domain-independent and anytime behavior adaptation process, called CASER (Case Adaptation by Segment Replanning), that finds an easyto-generate solution with low quality and adapts this solution to improve it. It follows the same idea as PbR. However, this method does not req ...
as a PDF
... network. In this figure, the vertices X and Y are constraints, and vertices R1 through R4 are initial events (roots). A parent set consists of one parent for X and one for Y (e.g., {P1(X), P3(Y)}, {P3(X), P3(Y)} ). The first part of the search iteration begins by computing the CMPPs for each parent ...
... network. In this figure, the vertices X and Y are constraints, and vertices R1 through R4 are initial events (roots). A parent set consists of one parent for X and one for Y (e.g., {P1(X), P3(Y)}, {P3(X), P3(Y)} ). The first part of the search iteration begins by computing the CMPPs for each parent ...
On Multi-Robot Area Coverage
... Reduced-CDT and Reduced-Vis based on the Constrained Delaunay Triangulation and the Visibility Graph are introduced so as to model the structure of the target area more efficiently. Also, due to the distributed characteristic of the coverage problem, another algorithm called Multi-Prim’s is applied ...
... Reduced-CDT and Reduced-Vis based on the Constrained Delaunay Triangulation and the Visibility Graph are introduced so as to model the structure of the target area more efficiently. Also, due to the distributed characteristic of the coverage problem, another algorithm called Multi-Prim’s is applied ...
APPLICATION OF ARTIFICIAL INTELLIGENCE METHODS IN
... called parents), crossover (take a copy of the selected parents and apply a crossover operation on them, with a certain probability, to produce two new children), and mutation (after the crossover operation, the two children are produced and later mutated with a certain probability to produce two ne ...
... called parents), crossover (take a copy of the selected parents and apply a crossover operation on them, with a certain probability, to produce two new children), and mutation (after the crossover operation, the two children are produced and later mutated with a certain probability to produce two ne ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.