TY - GEN
T1 - Solving a spatial puzzle using answer set programming integrated with Markov decision process
AU - Freitas Dos Santos, Thiago
AU - Santos, Paulo
AU - Ferreira, Leonardo
AU - Bianchi, Reinaldo
AU - Cabalar, Pedro
PY - 2018/12/17
Y1 - 2018/12/17
N2 - Spatial puzzles are interesting domains to investigate problem solving, since the reasoning processes involved in reasoning about spatial knowledge is one of the essential items for an agent to interact in the human environment. With this in mind, the goal of this work is to investigate the knowledge representation and reasoning process related to the solution of a spatial puzzle, the Fisherman's Folly, composed of flexible string, rigid objects and holes. To achieve this goal, the present paper uses heuristics (obtained after solving a relaxed version of the puzzle) to accelerate the learning process, while applying a method that combines Answer Set programming (ASP) with Reinforcement learning (RL), the oASP(MDP) algorithm, to find a solution to the puzzle. ASP is the logic language chosen to build the set of states and actions of a Markov Decision Process (MDP) representing the domain, where RL is used to learn the optimal policy of the problem.
AB - Spatial puzzles are interesting domains to investigate problem solving, since the reasoning processes involved in reasoning about spatial knowledge is one of the essential items for an agent to interact in the human environment. With this in mind, the goal of this work is to investigate the knowledge representation and reasoning process related to the solution of a spatial puzzle, the Fisherman's Folly, composed of flexible string, rigid objects and holes. To achieve this goal, the present paper uses heuristics (obtained after solving a relaxed version of the puzzle) to accelerate the learning process, while applying a method that combines Answer Set programming (ASP) with Reinforcement learning (RL), the oASP(MDP) algorithm, to find a solution to the puzzle. ASP is the logic language chosen to build the set of states and actions of a Markov Decision Process (MDP) representing the domain, where RL is used to learn the optimal policy of the problem.
KW - answer set programming
KW - heuristic
KW - oASP(MDP)
KW - reinforcement learning
KW - spatial puzzle
UR - http://www.scopus.com/inward/record.url?scp=85060854756&partnerID=8YFLogxK
U2 - 10.1109/BRACIS.2018.00097
DO - 10.1109/BRACIS.2018.00097
M3 - Conference contribution
AN - SCOPUS:85060854756
T3 - Proceedings - 2018 Brazilian Conference on Intelligent Systems, BRACIS 2018
SP - 528
EP - 533
BT - Proceedings - 2018 Brazilian Conference on Intelligent Systems, BRACIS 2018
PB - Institute of Electrical and Electronics Engineers
T2 - 2018 Brazilian Conference on Intelligent Systems, BRACIS 2018
Y2 - 22 October 2018 through 25 October 2018
ER -