In this paper, we propose a new double-layered state space construction method, which consists of Fritzke's Growing Neural Gas algorithm and a class management mechanism of GNG units. The classification algorithm yields a new class by referring to anticipation error, anticipation vectors of an originated class, and anticipation vectors GNG units belonging in the originated class.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil= kn-affil=Okayama University en-keyword=Reinforcement Learning kn-keyword=Reinforcement Learning en-keyword=Growing Neural Gas kn-keyword=Growing Neural Gas en-keyword=Incremental State Space Construction kn-keyword=Incremental State Space Construction END start-ver=1.4 cd-journal=joma no-vol=1 cd-vols= no-issue= article-no= start-page=158 end-page=165 dt-received= dt-revised= dt-accepted= dt-pub-year=2005 dt-pub=20050902 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Dynamic salting route optimisation using evolutionary computation en-subtitle= kn-subtitle= en-abstract= kn-abstract=On marginal winter nights, highway authorities face a difficult decision as to whether or not to salt the road network. The consequences of making a wrong decision are serious, as an untreated network is a major hazard. However, if salt is spread when it is not actually required, there are unnecessary financial and environmental consequences. In this paper, a new salting route optimisation system is proposed which combines evolutionary computation (EC) with the next generation road weather information systems (XRWIS). XRWIS is a new high resolution forecast system which predicts road surface temperature and condition across the road network over a 24 hour period. ECs are used to optimise a series of salting routes for winter gritting by considering XRWIS temperature data along with treatment vehicle and road network constraints. This synergy realises daily dynamic routing and it will yield considerable benefits for areas with a marginal ice problem.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ChapmanLee en-aut-sei=Chapman en-aut-mei=Lee kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YaoXin en-aut-sei=Yao en-aut-mei=Xin kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=University of Birmingham, UK affil-num=3 en-affil= kn-affil=University of Birmingham, UK en-keyword=decision making kn-keyword=decision making en-keyword=evolutionary computation kn-keyword=evolutionary computation en-keyword=geographic information systems kn-keyword=geographic information systems en-keyword=land surface temperature kn-keyword=land surface temperature en-keyword=optimisation kn-keyword=optimisation en-keyword=road safety kn-keyword=road safety en-keyword=traffic information systems kn-keyword=traffic information systems en-keyword=weather forecasting kn-keyword=weather forecasting END start-ver=1.4 cd-journal=joma no-vol=2 cd-vols= no-issue= article-no= start-page=1115 end-page=1120 dt-received= dt-revised= dt-accepted= dt-pub-year=2004 dt-pub=20047 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=State space construction of reinforcement learning agents based upon anticipated sensory changes en-subtitle= kn-subtitle= en-abstract= kn-abstract=We propose herein a new incremental state construction method which consists of Fritzke's growing neural gas algorithm and a class management mechanism of GNG units. The GNG algorithm condenses sensory inputs and learns which areas are frequently sensed. The CMM yields a new state based upon the anticipated behaviors of the agent, i.e., a couple of actions by an agent and the resultant change in sensory inputs. Computational simulations on the mountain-car task confirm the effectiveness of the proposed method.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil= kn-affil=Okayama University en-keyword=learning (artificial intelligence) kn-keyword=learning (artificial intelligence) en-keyword=neural nets kn-keyword=neural nets en-keyword=state-space methods kn-keyword=state-space methods END start-ver=1.4 cd-journal=joma no-vol=2 cd-vols= no-issue= article-no= start-page=1213 end-page=1219 dt-received= dt-revised= dt-accepted= dt-pub-year=2001 dt-pub=20015 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Coevolutionary GA with schema extraction by machine learning techniques and its application to knapsack problems en-subtitle= kn-subtitle= en-abstract= kn-abstract=The authors introduce a novel coevolutionary genetic algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in a natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm the effectiveness of the proposed method
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HoriuchiTadashi en-aut-sei=Horiuchi en-aut-mei=Tadashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KanekoTakeshi en-aut-sei=Kaneko en-aut-mei=Takeshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Osaka University affil-num=3 en-affil= kn-affil=Kyoto University affil-num=4 en-affil= kn-affil=Kyoto University affil-num=5 en-affil= kn-affil=Okayama University affil-num=6 en-affil= kn-affil=Okayama University en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=knapsack problems kn-keyword=knapsack problems en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=search problems kn-keyword=search problems END start-ver=1.4 cd-journal=joma no-vol=1 cd-vols= no-issue=1 article-no= start-page=6 end-page=9 dt-received= dt-revised= dt-accepted= dt-pub-year=2006 dt-pub=20062 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Robust route optimization for gritting/salting trucks: a CERCIA experience en-subtitle= kn-subtitle= en-abstract= kn-abstract=Highway authorities in marginal winter climates are responsible for the precautionary gritting/salting of the road network in order to prevent frozen roads. For efficient and effective road maintenance, accurate road surface temperature prediction is required. However, this information is useless if an effective means of utilizing this information is unavailable. This is where gritting route optimization plays a crucial role. The decision whether to grit the road network at marginal nights is a difficult problem. The consequences of making a wrong decision are serious, as untreated roads are a major hazard. However, if grit/salt is spread when it is not actually required, there are unnecessary financial and environmental costs. The goal here is to minimize the financial and environmental costs while ensuring roads that need treatment will. In this article, a salting route optimization (SRO) system that combines evolutionary algorithms with the neXt generation Road Weather Information System (XRWIS) is introduced. The synergy of these methodologies means that salting route optimization can be done at a level previously not possible.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ChapmanLee en-aut-sei=Chapman en-aut-mei=Lee kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YaoXin en-aut-sei=Yao en-aut-mei=Xin kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=University of Birmingham, UK affil-num=3 en-affil= kn-affil=University of Birmingham, UK en-keyword=Robust route optimization for gritting/salting trucks: a CERCIA experience kn-keyword=Robust route optimization for gritting/salting trucks: a CERCIA experience END start-ver=1.4 cd-journal=joma no-vol=4 cd-vols= no-issue= article-no= start-page=2732 end-page=2737 dt-received= dt-revised= dt-accepted= dt-pub-year=2000 dt-pub=200010 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=An incremental state-segmentation method for reinforcement learning using ART neural network en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this paper, we propose a new incremental state segmentation method by utilizing information of the agents' state transition table which consists of a tuple of (state; action, state) in order to reduce the effort of designers and which is generated using the ART neural network. In the proposed method, if an inconsistent situation in the state transition table is observed, agents refine their map from perceptual inputs to states such that inconsistency is resolved. We introduce two kinds of inconsistency, i.e., different results caused by the same states and the same actions, and contradiction due to ambiguous states. Several computational simulations on cart-pole problems confirm the effectiveness of the proposed method
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NinomiyaAkira en-aut-sei=Ninomiya en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HoriuchiTadashi en-aut-sei=Horiuchi en-aut-mei=Tadashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Osaka University affil-num=4 en-affil= kn-affil=Okayama University affil-num=5 en-affil= kn-affil=Okayama University en-keyword=ART neural nets kn-keyword=ART neural nets en-keyword=digital simulation kn-keyword=digital simulation en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=software agents kn-keyword=software agents END start-ver=1.4 cd-journal=joma no-vol=4 cd-vols= no-issue= article-no= start-page=2405 end-page=2410 dt-received= dt-revised= dt-accepted= dt-pub-year=2001 dt-pub=20017 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Perception-action rule acquisition by coevolutionary fuzzy classifier system en-subtitle= kn-subtitle= en-abstract= kn-abstract=Recently, many researchers have studied the techniques in applying a fuzzy classifier system (FCS) to control mobile robots, since the FCS can easily treat continuous inputs, such as sensors and images by using a fuzzy number. By using the FCS, however, only reflective rules are acquired. Thus, in the proposed approach, an additional genetic algorithm is incorporated in order to search for strategic knowledge, i.e., the sequence of effective activated rules in the FCS. Therefore, the proposed method consists of two modules: an ordinal FCS and the genetic algorithm. Computational experiments based on WEBOTS, one of the Khepera robot simulators, confirm the effectiveness of the proposed method
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NodaTakashi en-aut-sei=Noda en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University affil-num=5 en-affil= kn-affil=Kyoto University en-keyword=fuzzy control kn-keyword=fuzzy control en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=knowledge based systems kn-keyword=knowledge based systems en-keyword=mobile robots kn-keyword=mobile robots en-keyword=pattern classification kn-keyword=pattern classification en-keyword=search problems kn-keyword=search problems END start-ver=1.4 cd-journal=joma no-vol=3 cd-vols= no-issue= article-no= start-page=1609 end-page=1612 dt-received= dt-revised= dt-accepted= dt-pub-year=2002 dt-pub=20028 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evolutionary constitution of game player agents en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this paper, we propose a constitution method of game player agent that adopts a neural network as a state evaluation function for the game player, and evolves its weights and structure by evolutionary strategy. In this method, we attempt to acquire a better state evaluation function by evolving weights and structure simultaneously.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HoriuchiTadashi en-aut-sei=Horiuchi en-aut-mei=Tadashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Matsue National College of Technology en-keyword=Evolutionary Computations kn-keyword=Evolutionary Computations en-keyword=State Evaluation Function kn-keyword=State Evaluation Function en-keyword=Neural Networks kn-keyword=Neural Networks END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=3098 end-page=3105 dt-received= dt-revised= dt-accepted= dt-pub-year=2006 dt-pub=20067 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Robust Solution of Salting Route Optimisation Using Evolutionary Algorithms en-subtitle= kn-subtitle= en-abstract= kn-abstract=The precautionary salting of the road network is an important maintenance issue for countries with a marginal winter climate. On many nights, not all the road network will require treatment as the local geography will mean some road sections are warmer than others. Hence, there is a logic to optimising salting routes based on known road surface temperature distributions. In this paper, a robust solution of Salting Route Optimisation using a training dataset of daily predicted temperature distributions is proposed. Evolutionary Algorithms are used to produce salting routes which group together the colder sections of the road network. Financial savings can then be made by not treating the warmer routes on the more marginal of nights. Experimental results on real data also reveal that the proposed methodology reduced total distance traveled on the new routes by around 10conventional salting routes.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=LinDan en-aut-sei=Lin en-aut-mei=Dan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ChapmanLee en-aut-sei=Chapman en-aut-mei=Lee kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=YaoXin en-aut-sei=Yao en-aut-mei=Xin kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Tianjin University affil-num=3 en-affil= kn-affil=University of Birmingham, UK affil-num=4 en-affil= kn-affil=University of Birmingham, UK END start-ver=1.4 cd-journal=joma no-vol=2 cd-vols= no-issue= article-no= start-page=1184 end-page=1189 dt-received= dt-revised= dt-accepted= dt-pub-year=2000 dt-pub=20007 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A new fitness function for discovering a lot of satisfiable solutions in constraint satisfaction problems en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this paper, we discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Kyoto University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University en-keyword=constraint theory kn-keyword=constraint theory en-keyword=functions kn-keyword=functions en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=operations research kn-keyword=operations research END start-ver=1.4 cd-journal=joma no-vol=1 cd-vols= no-issue= article-no= start-page=436 end-page=439 dt-received= dt-revised= dt-accepted= dt-pub-year=2003 dt-pub=200312 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Estimation of Bayesian network algorithm with GA searching for better network structure en-subtitle= kn-subtitle= en-abstract= kn-abstract=Estimation of Bayesian network algorithms, which adopt Bayesian networks as the probabilistic model were one of the most sophisticated algorithms in the estimation of distribution algorithms. However the estimation of Bayesian network is key topic of this algorithm, conventional EBNAs adopt greedy searches to search for better network structures. In this paper, we propose a new EBNA, which adopts genetic algorithm to search the structure of Bayesian network. In order to reduce the computational complexity of estimating better network structures, we elaborates the fitness function of the GA module, based upon the synchronicity of specific pattern in the selected individuals. Several computational simulations on multidimensional knapsack problems show us the effectiveness of the proposed method.
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Kyoto University en-keyword=belief networks kn-keyword=belief networks en-keyword=computational complexity kn-keyword=computational complexity en-keyword=distributed algorithms kn-keyword=distributed algorithms en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=knapsack problems kn-keyword=knapsack problems en-keyword=probability kn-keyword=probability END start-ver=1.4 cd-journal=joma no-vol=3 cd-vols= no-issue= article-no= start-page=1436 end-page=1441 dt-received= dt-revised= dt-accepted= dt-pub-year=2001 dt-pub=200110 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Adaptive state construction for reinforcement learning and its application to robot navigation problems en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper applies our state construction method by ART neural network to robot navigation problems. Agents in this paper consist of ART neural network and contradiction resolution mechanism. The ART neural network serves as a mean of state recognition which maps stimulus inputs to a certain state and state construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the contradiction resolution mechanism (CRM) uses agents' state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: "Different results caused by the same states and the same actions" and "Contradiction due to ambiguous states." The simulation results on the robot navigation problems confirm the effectiveness of the proposed method
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NinomiyaAkira en-aut-sei=Ninomiya en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HoriuchiTadashi en-aut-sei=Horiuchi en-aut-mei=Tadashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KonishiTadataka en-aut-sei=Konishi en-aut-mei=Tadataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Matsue National College of Technology affil-num=4 en-affil= kn-affil=Chugoku Polytechnic College affil-num=5 en-affil= kn-affil=Okayama University en-keyword=Adaptive State Construction kn-keyword=Adaptive State Construction en-keyword=ART Neural Network kn-keyword=ART Neural Network en-keyword=Reinforcement Learning kn-keyword=Reinforcement Learning END start-ver=1.4 cd-journal=joma no-vol=4 cd-vols= no-issue= article-no= start-page=2935 end-page=2940 dt-received= dt-revised= dt-accepted= dt-pub-year=2000 dt-pub=200010 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Constraint satisfaction on dynamic environments by the means of coevolutionary genetic algorithms en-subtitle= kn-subtitle= en-abstract= kn-abstract=We discuss adaptability of evolutionary computations in dynamic environments. We introduce two classes of dynamic environments which are utilizing the notion of constraint satisfaction problems: changeover and gradation. The changeover environment is a problem class which consists of a sequence of the constraint networks with the same nature. On the other hand, the gradation environment is a problem class which consists of a sequence of the constraint networks such that the sequence is associated with two constraint networks, i. e., initial and target, and all constraint networks in the sequence metamorphosis from the initial constraint network to the target constraint network. We compare coevolutionary genetic algorithms with SGA in computational simulations. Experimental results on the above dynamic environments confirm us the effectiveness of our approach, i.e., coevolutionary genetic algorithm
en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KataiOsamu en-aut-sei=Katai en-aut-mei=Osamu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KonishiTadaaki en-aut-sei=Konishi en-aut-mei=Tadaaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=BabaMitsuru en-aut-sei=Baba en-aut-mei=Mitsuru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Kyoto University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University en-keyword=constraint theory kn-keyword=constraint theory en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword=operations research kn-keyword=operations research END start-ver=1.4 cd-journal=joma no-vol=2009 cd-vols= no-issue=1 article-no= start-page=143 end-page=146 dt-received= dt-revised= dt-accepted= dt-pub-year=2009 dt-pub=20091112 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evolving FPS Game Players by Using Continuous EDA-RL en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper extends EDA-RL, Estimation of Distribution Algorithms for Reinforcement Learning Problems, to continuous domain. The extended EDA-RL is used to constitiute FPS game players. In order to cope with continuous input-output relations, Gaussian Network is employed as in EBNA. Simulation results on Unreal Tournament 2004, one of major FPS games, confirm the effectiveness of the proposed method. en-copyright= kn-copyright= en-aut-name= en-aut-sei= en-aut-mei= kn-aut-name=TsubotaHajime kn-aut-sei=Tsubota kn-aut-mei=Hajime aut-affil-num=1 ORCID= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name=半田久志 kn-aut-sei=半田 kn-aut-mei=久志 aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University END start-ver=1.4 cd-journal=joma no-vol=2009 cd-vols= no-issue=1 article-no= start-page=3 end-page=7 dt-received= dt-revised= dt-accepted= dt-pub-year=2009 dt-pub=20091110 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Rule Induction by EDA with Instance-Subpopulations en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this paper, a new rule induction method by using EDA with instance-subpopulations is proposed. The proposed method introduces a notion of instance-subpopulation, where a set of individuals matching a training instance. Then, EDA procedure is separately carried out for each instance-subpopulation. Individuals generated by each EDA procedure are merged to constitute the population at the next generation. We examined the proposed method on Breast-cancer in Wisconsin and Chess End-Game. The comparisons with other algorithms show the effectiveness of the proposed method. en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name=半田久志 kn-aut-sei=半田 kn-aut-mei=久志 aut-affil-num=1 ORCID= affil-num=1 en-affil= kn-affil=Okayama University END start-ver=1.4 cd-journal=joma no-vol=2008 cd-vols= no-issue=1 article-no= start-page=185 end-page=190 dt-received= dt-revised= dt-accepted= dt-pub-year=2008 dt-pub=20081211 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Rule Acquisition for Cognitive Agents by Using Estimation of Distribution Algorithms en-subtitle= kn-subtitle= en-abstract= kn-abstract=Cognitive Agents must be able to decide their actions based on their recognized states. In general, learning mechanisms are equipped for such agents in order to realize intellgent behaviors. In this paper, we propose a new Estimation of Distribution Algorithms (EDAs) which can acquire effective rules for cognitive agents. Basic calculation procedure of the EDAs is that 1) select better individuals, 2) estimate probabilistic models, and 3) sample new individuals. In the proposed method, instead of the use of individuals, input-output records in episodes are directory used for estimating the probabilistic model by Conditional Random Fields. Therefore, estimated probabilistic model can be regarded as policy so that new input-output records are generated by the interaction between the policy and environments. Computer simulations on Probabilistic Transition Problems show the effectiveness of the proposed method. en-copyright= kn-copyright= en-aut-name=NishimuraTokue en-aut-sei=Nishimura en-aut-mei=Tokue kn-aut-name=西村徳栄 kn-aut-sei=西村 kn-aut-mei=徳栄 aut-affil-num=1 ORCID= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name=半田久志 kn-aut-sei=半田 kn-aut-mei=久志 aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Graduate School of Natural Science and Technology, Okayama University affil-num=2 en-affil= kn-affil=Graduate School of Natural Science and Technology, Okayama University END start-ver=1.4 cd-journal=joma no-vol=2008 cd-vols= no-issue=1 article-no= start-page=48 end-page=53 dt-received= dt-revised= dt-accepted= dt-pub-year=2008 dt-pub=200812 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Constitution of Ms.PacMan Player with Critical-Situation Learning Mechanism en-subtitle= kn-subtitle= en-abstract= kn-abstract=We previously proposed evolutionary fuzzy systems of playing Ms.PacMan for the competitions. As a consequence of the evolution, reflective action rules such that PacMan tries to eat pills effectively until ghosts come close to PacMan are acquired. Such rules works well. However, sometimes it is too reflective so that PacMan go toward ghosts by herself in longer corridors. In this paper, a critical situation learning module is combined with the evolved fuzzy systems, i.e., reflective action module. The critical situation learning module is composed of Q-learning with CMAC. Location information of surrounding ghosts and the existence of power-pills are given to PacMan as state. This module punishes if PacMan is caught by ghosts. Therefore, this module learning which pairs of (state, action) cause her death. By using learnt Q-value, PacMan tries to survive much longer. Experimental results on Ms.PacMan elucidate the proposed method is promising since it can capture critical situations well. However, as a consequence of the large amount of memory required by CMAC, real time responses tend to be lost. en-copyright= kn-copyright= en-aut-name=HandaHisashi en-aut-sei=Handa en-aut-mei=Hisashi kn-aut-name=半田久志 kn-aut-sei=半田 kn-aut-mei=久志 aut-affil-num=1 ORCID= affil-num=1 en-affil= kn-affil=Graduate School of Natural Science and Technology Okayama University END