Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-80
K. Rocki, R. Suda
Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search. It can theoretically be applied to any domain that can be described in terms of state, action pairs and simulation used to forecast outcomes such as decision support, control, delayed reward problems or complex optimization. The motivation behind this work is caused by the emerging GPU-based systems and their high computational potential combined with relatively low power usage compared to CPUs. As a problem to be solved we chose to develop an AI GPU(Graphics Processing Unit)-based agent in the game of Reversi (Othello) which provides a sufficiently complex problem for tree searching with non-uniform structure and an average branching factor of over 8. We present an efficient parallel GPU MCTS implementation based on the introduced ’block-parallelism’ scheme which combines GPU SIMD thread groups and performs independent searches without any need of intra-GPU or inter-GPU communication. We compare it with a simple leaf parallel scheme which implies certain performance limitations. The obtained results show that using my GPU MCTS implementation on the TSUBAME 2.0 system one GPU can be compared to 100-200 CPU threads depending on factors such as the search time and other MCTS parameters in terms of obtained results. We propose and analyze simultaneous CPU/GPU execution which improves the overall result.
{"title":"Parallel Monte Carlo Tree Search on GPU","authors":"K. Rocki, R. Suda","doi":"10.3233/978-1-60750-754-3-80","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-80","url":null,"abstract":"Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search. It can theoretically be applied to any domain that can be described in terms of state, action pairs and simulation used to forecast outcomes such as decision support, control, delayed reward problems or complex optimization. The motivation behind this work is caused by the emerging GPU-based systems and their high computational potential combined with relatively low power usage compared to CPUs. As a problem to be solved we chose to develop an AI GPU(Graphics Processing Unit)-based agent in the game of Reversi (Othello) which provides a sufficiently complex problem for tree searching with non-uniform structure and an average branching factor of over 8. We present an efficient parallel GPU MCTS implementation based on the introduced ’block-parallelism’ scheme which combines GPU SIMD thread groups and performs independent searches without any need of intra-GPU or inter-GPU communication. We compare it with a simple leaf parallel scheme which implies certain performance limitations. The obtained results show that using my GPU MCTS implementation on the TSUBAME 2.0 system one GPU can be compared to 100-200 CPU threads depending on factors such as the search time and other MCTS parameters in terms of obtained results. We propose and analyze simultaneous CPU/GPU execution which improves the overall result.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116505230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-182
Asma Kilani
{"title":"Improving the Efficiency of Plan Optimisation Techniques","authors":"Asma Kilani","doi":"10.3233/978-1-61499-589-0-182","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-182","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122551022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-118
Petras Razanskas, A. Verikas, Charlotte Olsson, Per-Arne Viberg
A set of novel time-domain features characterizing multi-channel surface EMG (sEMG) signals of six muscles (rectus femoris, vastus lateralis, and semitendinosus of each leg) is proposed for prediction of physiological parameters considered important in cycling: blood lactate concentration and oxygen uptake. Fifty one different features, including phase shifts between muscles, active time percentages, sEMG amplitudes, as well as symmetry measures between both legs, were defined from sEMG data and used to train linear and random forest models. The random forests models achieved the coefficient of determination R2 = 0:962 (lactate) and R2 = 0:980 (oxygen). The linear models were less accurate. Feature pruning applied enabled creating accurate random forest models (R2 >0:9) using as few as 7 (lactate) or 4 (oxygen) time-domain features. sEMG amplitude was important for both types of models. Models to predict lactate also relied on measurements describing interaction between front and back muscles, while models to predict oxygen uptake relied on front muscles only, but also included interactions between the two legs.
{"title":"Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises","authors":"Petras Razanskas, A. Verikas, Charlotte Olsson, Per-Arne Viberg","doi":"10.3233/978-1-61499-589-0-118","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-118","url":null,"abstract":"A set of novel time-domain features characterizing multi-channel surface EMG (sEMG) signals of six muscles (rectus femoris, vastus lateralis, and semitendinosus of each leg) is proposed for prediction of physiological parameters considered important in cycling: blood lactate concentration and oxygen uptake. Fifty one different features, including phase shifts between muscles, active time percentages, sEMG amplitudes, as well as symmetry measures between both legs, were defined from sEMG data and used to train linear and random forest models. The random forests models achieved the coefficient of determination R2 = 0:962 (lactate) and R2 = 0:980 (oxygen). The linear models were less accurate. Feature pruning applied enabled creating accurate random forest models (R2 >0:9) using as few as 7 (lactate) or 4 (oxygen) time-domain features. sEMG amplitude was important for both types of models. Models to predict lactate also relied on measurements describing interaction between front and back muscles, while models to predict oxygen uptake relied on front muscles only, but also included interactions between the two legs.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"617 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117082733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-189
T. Houeland, Tore Bruland, A. Aamodt, H. Langseth
In complex domains, it is often necessary to determine which reasoning method would be the most appropriate for each task, and a combination of different methods has often shown the best results. We examine how two complementary reasoning methods, case-based reasoning and Bayesian networks, can be combined using metareasoning to form a more robust and better-performing system.
{"title":"Extended Abstract: Combining CBR and BN using metareasoning","authors":"T. Houeland, Tore Bruland, A. Aamodt, H. Langseth","doi":"10.3233/978-1-60750-754-3-189","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-189","url":null,"abstract":"In complex domains, it is often necessary to determine which reasoning method would be the most appropriate for each task, and a combination of different methods has often shown the best results. We examine how two complementary reasoning methods, case-based reasoning and Bayesian networks, can be combined using metareasoning to form a more robust and better-performing system.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128810869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-4
Christopher Nugent
{"title":"The Challenges with Intelligent Data Analysis in Smart Environments","authors":"Christopher Nugent","doi":"10.3233/978-1-61499-589-0-4","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-4","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125045501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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