Pub Date : 2015-12-01DOI: 10.3233/978-1-61499-589-0-186
J. Rafferty, C. Nugent, Jun Liu, Liming Luke Chen
Current ambient assistive living solutions have adopted a traditional sensor-centric approach, involving data analysis and activity recognition to provide assistance to individuals. The reliance on sensors and activity recognition in this approach introduces a number of issues. This study introduces a novel approach to assistive living which intends to address these issues via a paradigm shift from a sensor centric approach to a goal-oriented one. The goal-oriented approach focuses on identification of user goals in order to pro-actively offer assistance by either pre-defined or dynamically constructed video-based instruction. This extended abstract introduces the architecture of this goal-oriented approach, covers the novel developments required to realize it and discusses the current state of the research.
{"title":"Goal-driven, assistive agents for instructing and guiding user activities","authors":"J. Rafferty, C. Nugent, Jun Liu, Liming Luke Chen","doi":"10.3233/978-1-61499-589-0-186","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-186","url":null,"abstract":"Current ambient assistive living solutions have adopted a traditional sensor-centric approach, involving data analysis and activity recognition to provide assistance to individuals. The reliance on sensors and activity recognition in this approach introduces a number of issues. This study introduces a novel approach to assistive living which intends to address these issues via a paradigm shift from a sensor centric approach to a goal-oriented one. The goal-oriented approach focuses on identification of user goals in order to pro-actively offer assistance by either pre-defined or dynamically constructed video-based instruction. This extended abstract introduces the architecture of this goal-oriented approach, covers the novel developments required to realize it and discusses the current state of the research.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116312940","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 : 2015-04-28DOI: 10.3233/978-1-61499-589-0-157
Priyantha Wijayatunga
Conditioning on some set of confounders that causally affect both treatment and outcome variables can be sufficient for eliminating bias introduced by all such confounders when estimating causal effect of the treatment on the outcome from observational data. It is done by including them in propensity score model in so-called potential outcome framework for causal inference whereas in causal graphical modeling framework usual conditioning on them is done. However in the former framework, it is confusing when modeler finds a variable that is non-causally associated with both the treatment and the outcome. Some argue that such variables should also be included in the analysis for removing bias. But others argue that they introduce no bias so they should be excluded and conditioning on them introduces spurious dependence between the treatment and the outcome, thus resulting extra bias in the estimation. We show that there may be errors in both the arguments in different contexts. When such a variable is found neither of the actions may give the correct causal effect estimate. Selecting one action over the other is needed in order to be less wrong. We discuss how to select the better action.
{"title":"On Associative Confounder Bias","authors":"Priyantha Wijayatunga","doi":"10.3233/978-1-61499-589-0-157","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-157","url":null,"abstract":"Conditioning on some set of confounders that causally affect both treatment and outcome variables can be sufficient for eliminating bias introduced by all such confounders when estimating causal effect of the treatment on the outcome from observational data. It is done by including them in propensity score model in so-called potential outcome framework for causal inference whereas in causal graphical modeling framework usual conditioning on them is done. However in the former framework, it is confusing when modeler finds a variable that is non-causally associated with both the treatment and the outcome. Some argue that such variables should also be included in the analysis for removing bias. But others argue that they introduce no bias so they should be excluded and conditioning on them introduces spurious dependence between the treatment and the outcome, thus resulting extra bias in the estimation. We show that there may be errors in both the arguments in different contexts. When such a variable is found neither of the actions may give the correct causal effect estimate. Selecting one action over the other is needed in order to be less wrong. We discuss how to select the better action.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396820","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 : 2013-12-01DOI: 10.3233/978-1-61499-330-8-65
Rafael Cabañas, A. Cano, Manuel Gómez-Olmedo, A. Madsen
Finding an optimal elimination ordering is a NP-hard problem of crucial importance for the efficiency of the Influence Diagrams evaluation. Some of the traditional methods for determining the elimination ordering use heuristics that consider that potentials are represented as tables. However, if potentials are represented using binary trees traditional methods may not offer the best results. In the present paper, two new heuristics that consider that potentials are represented as binary trees are proposed. As a result, the storage requirements for evaluating an ID with binary trees is reduced.
{"title":"Heuristics for Determining the Elimination Ordering in the Influence Diagram Evaluation with Binary Trees","authors":"Rafael Cabañas, A. Cano, Manuel Gómez-Olmedo, A. Madsen","doi":"10.3233/978-1-61499-330-8-65","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-65","url":null,"abstract":"Finding an optimal elimination ordering is a NP-hard problem of crucial importance for the efficiency of the Influence Diagrams evaluation. Some of the traditional methods for determining the elimination ordering use heuristics that consider that potentials are represented as tables. However, if potentials are represented using binary trees traditional methods may not offer the best results. In the present paper, two new heuristics that consider that potentials are represented as binary trees are proposed. As a result, the storage requirements for evaluating an ID with binary trees is reduced.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129507184","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 : 2013-11-01DOI: 10.3233/978-1-61499-330-8-75
L. Chrpa, M. Vallati
In Automated Planning, learning and exploiting structural patterns of plans, domain models and/or problem models, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising, mainly because they are planner independent. This paper aims to extend and revisit the recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate ‘achievements‘ or ‘requirements’, in order to bring new theoretical results (PSPACE completeness of deciding inner entanglements), present a new way of encoding of inner entanglements and empirical comparison between different kinds of inner entanglements.
{"title":"Revisiting Inner Entanglements in Classical Planning","authors":"L. Chrpa, M. Vallati","doi":"10.3233/978-1-61499-330-8-75","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-75","url":null,"abstract":"In Automated Planning, learning and exploiting structural patterns of plans, domain models and/or problem models, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising, mainly because they are planner independent. This paper aims to extend and revisit the recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate ‘achievements‘ or ‘requirements’, in order to bring new theoretical results (PSPACE completeness of deciding inner entanglements), present a new way of encoding of inner entanglements and empirical comparison between different kinds of inner entanglements.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764202","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 : 2013-06-01DOI: 10.3233/978-1-61499-330-8-215
J. Peña
Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose structure depends on the CG. However, the CG represents the errors implicitly, as no nodes in the CG correspond to the errors. We propose in this paper to add some deterministic nodes to the CG in order to represent the errors explicitly. We call the result an EAMP CG. We will show that, as desired, every AMP CG is Markov equivalent to its corresponding EAMP CG under marginalization of the error nodes. We will also show that every EAMP CG under marginalization of the error nodes is Markov equivalent to some LWF CG under marginalization of the error nodes, and that the latter is Markov equivalent to some directed and acyclic graph (DAG) under marginalization of the error nodes and conditioning on some selection nodes. This is important because it implies that the independence model represented by an AMP CG can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we will show that EAMP CGs are closed under marginalization. This is a desirable feature because it guarantees parsimonious models under marginalization.
{"title":"Error AMP Chain Graphs","authors":"J. Peña","doi":"10.3233/978-1-61499-330-8-215","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-215","url":null,"abstract":"Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose structure depends on the CG. However, the CG represents the errors implicitly, as no nodes in the CG correspond to the errors. We propose in this paper to add some deterministic nodes to the CG in order to represent the errors explicitly. We call the result an EAMP CG. We will show that, as desired, every AMP CG is Markov equivalent to its corresponding EAMP CG under marginalization of the error nodes. We will also show that every EAMP CG under marginalization of the error nodes is Markov equivalent to some LWF CG under marginalization of the error nodes, and that the latter is Markov equivalent to some directed and acyclic graph (DAG) under marginalization of the error nodes and conditioning on some selection nodes. This is important because it implies that the independence model represented by an AMP CG can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we will show that EAMP CGs are closed under marginalization. This is a desirable feature because it guarantees parsimonious models under marginalization.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004343","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 : 2011-05-01DOI: 10.3233/978-1-60750-754-3-60
L. Chrpa
Trajectory (path) planning is a well known and thoroughly studied field of automated planning. It is usually used in computer games, robotics or autonomous agent simulations. Grids are often used for regular discretization of continuous space. Many methods exist for trajectory (path) planning on grids, we address the well known A* algorithm and the state-of-the-art Theta* algorithm. Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. In this paper, we provide an extension of both these algorithms to enable support for speed limit constraints.We experimentally evaluate and thoroughly discuss how the extensions affect the planning process showing reasonability and justification of our approach.
{"title":"Trajectory Planning on Grids: Considering Speed Limit Constraints","authors":"L. Chrpa","doi":"10.3233/978-1-60750-754-3-60","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-60","url":null,"abstract":"Trajectory (path) planning is a well known and thoroughly studied field \u0000of automated planning. It is usually used in computer games, robotics or autonomous \u0000agent simulations. Grids are often used for regular discretization of continuous \u0000space. Many methods exist for trajectory (path) planning on grids, we \u0000address the well known A* algorithm and the state-of-the-art Theta* algorithm. \u0000Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. \u0000In this paper, we provide an extension of both these algorithms to enable \u0000support for speed limit constraints.We experimentally evaluate and thoroughly discuss \u0000how the extensions affect the planning process showing reasonability and justification \u0000of our approach.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272401","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 : 2001-02-19DOI: 10.1007/3-540-45493-4_84
E. Tipans, A. Borisov
{"title":"Some Possibilities of Improving the CORA Classification Algorithm","authors":"E. Tipans, A. Borisov","doi":"10.1007/3-540-45493-4_84","DOIUrl":"https://doi.org/10.1007/3-540-45493-4_84","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131625693","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 : 1998-08-01DOI: 10.1142/9789812816849_0011
R. Neville
{"title":"Partially Precalculated Weights for Backpropagation Training of RAM-Based sigma-pi Nets","authors":"R. Neville","doi":"10.1142/9789812816849_0011","DOIUrl":"https://doi.org/10.1142/9789812816849_0011","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132926858","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: 10.3233/978-1-60750-754-3-173
Magnus Johnsson, D. G. Méndez, G. Hesslow, C. Balkenius
{"title":"Internal Simulation in a Bimodal System","authors":"Magnus Johnsson, D. G. Méndez, G. Hesslow, C. Balkenius","doi":"10.3233/978-1-60750-754-3-173","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-173","url":null,"abstract":"","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":"115132263","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: 10.3233/978-1-60750-754-3-7
T. Ågotnes
{"title":"Coordinating multi-agent systems using social laws","authors":"T. Ågotnes","doi":"10.3233/978-1-60750-754-3-7","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-7","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"9 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120925767","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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