Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-85
Huu-Phuoc Duong, Thach-Thao Duong, D. Pham, A. Sattar, A. Duong
This paper presents an efficient trap escape strategy in stochastic local search for Satisfiability. The proposed method aims to enhance local search by pro- viding an alternative local minima escaping strategy. Our variable selection scheme provides a novel local minima escaping mechanism to explore new solution areas. Conflict variables are hypothesized as variables recently selected near local min- ima. Hence, a list of backtracked conflict variables is retrieved from local min- ima. The new strategy selects variables in the backtracked variable list based on the clause-weight scoring function and stagnation weights and variable weights as tiebreak criteria. This method is an alternative to the conventional method of se- lecting variables in a randomized unsatisfied clause. The proposed tiebreak method favors high stagnation weights and low variable weights during trap escape phases. The new strategies are examined on verification benchmark and SAT Competi- tion 2011 and 2012 application and crafted instances. Our experiments show that proposed strategy has comparable performance with state-of-the-art local search solvers for SAT.
{"title":"Trap escape for local search by backtracking and conflict reverse","authors":"Huu-Phuoc Duong, Thach-Thao Duong, D. Pham, A. Sattar, A. Duong","doi":"10.3233/978-1-61499-330-8-85","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-85","url":null,"abstract":"This paper presents an efficient trap escape strategy in stochastic local search for Satisfiability. The proposed method aims to enhance local search by pro- viding an alternative local minima escaping strategy. Our variable selection scheme provides a novel local minima escaping mechanism to explore new solution areas. Conflict variables are hypothesized as variables recently selected near local min- ima. Hence, a list of backtracked conflict variables is retrieved from local min- ima. The new strategy selects variables in the backtracked variable list based on the clause-weight scoring function and stagnation weights and variable weights as tiebreak criteria. This method is an alternative to the conventional method of se- lecting variables in a randomized unsatisfied clause. The proposed tiebreak method favors high stagnation weights and low variable weights during trap escape phases. The new strategies are examined on verification benchmark and SAT Competi- tion 2011 and 2012 application and crafted instances. Our experiments show that proposed strategy has comparable performance with state-of-the-art local search solvers for SAT.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"8 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":"122525480","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: 10.3233/978-1-61499-589-0-184
E. Lagerstedt, M. Riveiro, Serge Thill
{"title":"Interacting with Artificial Agents","authors":"E. Lagerstedt, M. Riveiro, Serge Thill","doi":"10.3233/978-1-61499-589-0-184","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-184","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"37 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":"134215064","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: 10.3233/978-1-60750-754-3-163
M. Haage, J. Malec, Anders Nilsson, K. Nilsson, Sławomir Nowaczyk
This article describes results of the work on knowledge representation techniques chosen for use in the European project SIARAS (Skill-Based Inspection and Assembly for Reconfigurable Automation Systems). Its goal was to create intelligent support system for reconfiguration and adaptation of robot-based manufacturing cells. Declarative knowledge is represented first of all in an ontology expressed in OWL, for a generic taxonomical reasoning, and in a number of special-purpose reasoning modules, specific for the application domain. The domain/dependent modules are organized in a blackboard-like architecture.
{"title":"Declarative-knowledge-based reconfiguration of automation systems using a blackboard architecture","authors":"M. Haage, J. Malec, Anders Nilsson, K. Nilsson, Sławomir Nowaczyk","doi":"10.3233/978-1-60750-754-3-163","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-163","url":null,"abstract":"This article describes results of the work on knowledge representation techniques chosen for use in the European project SIARAS (Skill-Based Inspection and Assembly for Reconfigurable Automation Systems). Its goal was to create intelligent support system for reconfiguration and adaptation of robot-based manufacturing cells. Declarative knowledge is represented first of all in an ontology expressed in OWL, for a generic taxonomical reasoning, and in a number of special-purpose reasoning modules, specific for the application domain. The domain/dependent modules are organized in a blackboard-like architecture.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"44 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":"123861785","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: 10.3233/978-1-61499-330-8-303
Chiara Zecchin, A. Facchinetti, G. Sparacino, C. Cobelli
{"title":"Neural Network for Prediction of Glucose Concentration in Type 1 Diabetic Patients","authors":"Chiara Zecchin, A. Facchinetti, G. Sparacino, C. Cobelli","doi":"10.3233/978-1-61499-330-8-303","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-303","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"498 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":"116381664","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: 10.3233/978-1-61499-330-8-245
Gleb Sizov, Pınar Öztürk
We present a method that applies association rule mining for information retrieval. Our approach is different from traditional information retrieval since retrieval is done based on association rather than similarity, which might be useful for knowledge discovery purposes such as finding an explanation or elaboration for an event in a collection of domain-specific documents. The method proposed in this paper is based on the SmoothApriori algorithm which accommodates similarity in the association rule mining process to mine association rules between sentences or larger text units. We introduce query-focused association rule mining that allows association-based retrieval from larger amount of data than with a traditional association-rule mining approach. Combined with SmoothApriori, query-focused association rule mining provides association-based retrieval for textual data. This new method was evaluated on the task of automatically restoring sentences that were artificially removed from aviation investigation reports and showed significantly better results than any of our similarity-based retrieval baselines.
{"title":"Query-Focused Association Rule Mining for Information Retrieval","authors":"Gleb Sizov, Pınar Öztürk","doi":"10.3233/978-1-61499-330-8-245","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-245","url":null,"abstract":"We present a method that applies association rule mining for information retrieval. Our approach is different from traditional information retrieval since retrieval is done based on association rather than similarity, which might be useful for knowledge discovery purposes such as finding an explanation or elaboration for an event in a collection of domain-specific documents. The method proposed in this paper is based on the SmoothApriori algorithm which accommodates similarity in the association rule mining process to mine association rules between sentences or larger text units. We introduce query-focused association rule mining that allows association-based retrieval from larger amount of data than with a traditional association-rule mining approach. Combined with SmoothApriori, query-focused association rule mining provides association-based retrieval for textual data. This new method was evaluated on the task of automatically restoring sentences that were artificially removed from aviation investigation reports and showed significantly better results than any of our similarity-based retrieval baselines.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"2022 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":"127599229","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: 10.3233/978-1-61499-330-8-45
Bastian Bischoff, D. Nguyen-Tuong, Heiner Markert, A. Knoll
The 15-puzzle is a well-known game which has a long history stretching back in the 1870’s. The goal of the game is to arrange a shuffled set of 15 numbered tiles in ascending order, by sliding tiles into the one vacant space on a 4× 4 grid. In this paper, we study how Reinforcement Learning can be employed to solve the 15-puzzle problem. Mathematically, this problem can be described as a Markov Decision Process with the states being puzzle configurations. This leads to a large state space with approximately 10 elements. In order to deal with this large state space, we present a local variation of the Value-Iteration approach appropriate to solve the 15-puzzle starting from arbitrary configurations. Furthermore, we provide a theoretical analysis of the proposed strategy for solving the 15-puzzle problem. The feasibility of the approach and the plausibility of the analysis are additionally shown by simulation results.
{"title":"Solving the 15-Puzzle Game Using Local Value-Iteration","authors":"Bastian Bischoff, D. Nguyen-Tuong, Heiner Markert, A. Knoll","doi":"10.3233/978-1-61499-330-8-45","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-45","url":null,"abstract":"The 15-puzzle is a well-known game which has a long history stretching back in the 1870’s. The goal of the game is to arrange a shuffled set of 15 numbered tiles in ascending order, by sliding tiles into the one vacant space on a 4× 4 grid. In this paper, we study how Reinforcement Learning can be employed to solve the 15-puzzle problem. Mathematically, this problem can be described as a Markov Decision Process with the states being puzzle configurations. This leads to a large state space with approximately 10 elements. In order to deal with this large state space, we present a local variation of the Value-Iteration approach appropriate to solve the 15-puzzle starting from arbitrary configurations. Furthermore, we provide a theoretical analysis of the proposed strategy for solving the 15-puzzle problem. The feasibility of the approach and the plausibility of the analysis are additionally shown by simulation results.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"16 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":"127443823","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: 10.3233/978-1-60750-754-3-30
Pekka Naula, T. Pahikkala, A. Airola, T. Salakoski
{"title":"Learning Multi-Label Predictors under Sparsity Budget","authors":"Pekka Naula, T. Pahikkala, A. Airola, T. Salakoski","doi":"10.3233/978-1-60750-754-3-30","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-30","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"65 5 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":"122257802","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: 10.3233/978-1-61499-330-8-105
Sofie Therese Hansen, Carsten Stahlhut, L. K. Hansen
(08/12/2018) Expansion of the Variational Garrote to a Multiple Measurement Vectors Model The recovery of sparse signals in underdetermined systems is the focus of this paper. We propose an expanded version of the Variational Garrote, originally presented by Kappen (2011), which can use multiple measurement vectors (MMVs) to further improve source retrieval performance. We show its superiority compared to the original formulation and demonstrate its ability to correctly estimate both the sources’ location and their magnitude. Finally evidence is given of the high performance of the proposed algorithm compared to other MMV models.
{"title":"Expansion of the Variational Garrote to a Multiple Measurement Vectors Model","authors":"Sofie Therese Hansen, Carsten Stahlhut, L. K. Hansen","doi":"10.3233/978-1-61499-330-8-105","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-105","url":null,"abstract":"(08/12/2018) Expansion of the Variational Garrote to a Multiple Measurement Vectors Model The recovery of sparse signals in underdetermined systems is the focus of this paper. We propose an expanded version of the Variational Garrote, originally presented by Kappen (2011), which can use multiple measurement vectors (MMVs) to further improve source retrieval performance. We show its superiority compared to the original formulation and demonstrate its ability to correctly estimate both the sources’ location and their magnitude. Finally evidence is given of the high performance of the proposed algorithm compared to other MMV models.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"18 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":"132069532","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: 10.3233/978-1-61499-589-0-17
H. Borchani, Ana M. Martínez, A. Masegosa, H. Langseth, Thomas D. Nielsen, A. Salmerón, Antonio Fernández, A. Madsen, R. Sáez
Hanen BORCHANI a,1, Ana M. MARTINEZ a,2,1, Andres R. MASEGOSA b,1, Helge LANGSETH b, Thomas D. NIELSEN a, Antonio SALMERON c, Antonio FERNANDEZ d, Anders L. MADSEN a,e and Ramon SAEZ d aDepartment of Computer Science, Aalborg University, Denmark bDepartment of Computer and Information Science, The Norwegian University of Science and Technology, Norway cDepartment of Mathematics, University of Almeŕia, Spain dBanco de Credito Cooperativo, Spain eHUGIN EXPERT A/S, Aalborg, Denmark
Hanen BORCHANI a,1, Ana M. MARTINEZ a,2,1, Andres R. MASEGOSA b,1, Helge LANGSETH b, Thomas d . NIELSEN a, Antonio SALMERON c, Antonio FERNANDEZ d, Anders L. MADSEN a,e, Ramon SAEZ d .丹麦奥尔堡大学计算机科学系b .挪威科技大学计算机与信息科学系c . Almeŕia大学数学系，西班牙合作贷款银行，西班牙hugin EXPERT a /S，丹麦奥尔堡
{"title":"Dynamic Bayesian modeling for risk prediction in credit operations","authors":"H. Borchani, Ana M. Martínez, A. Masegosa, H. Langseth, Thomas D. Nielsen, A. Salmerón, Antonio Fernández, A. Madsen, R. Sáez","doi":"10.3233/978-1-61499-589-0-17","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-17","url":null,"abstract":"Hanen BORCHANI a,1, Ana M. MARTINEZ a,2,1, Andres R. MASEGOSA b,1, Helge LANGSETH b, Thomas D. NIELSEN a, Antonio SALMERON c, Antonio FERNANDEZ d, Anders L. MADSEN a,e and Ramon SAEZ d aDepartment of Computer Science, Aalborg University, Denmark bDepartment of Computer and Information Science, The Norwegian University of Science and Technology, Norway cDepartment of Mathematics, University of Almeŕia, Spain dBanco de Credito Cooperativo, Spain eHUGIN EXPERT A/S, Aalborg, Denmark","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"38 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":"115971903","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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