Floods are among the most destructive natural disasters, which are highly complex to model. The r... more Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literatur...
Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attem... more Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility. INDEX TERMS Case-based reasoning, data minimization, data privacy, data silos, decision support systems, federated architecture, federated case-based reasoning.
Aquaculture organizations establish facilities at the coast in Froya, Norway. The facilities bloc... more Aquaculture organizations establish facilities at the coast in Froya, Norway. The facilities block the surrounding area from fishing and cause environmental damage to close natural resources. Fishers who depend on those natural resources get the opportunity to influence the aquaculture expansion through complaints about the municipality’s coastal plan. Statistics show that fishers don’t complain as much as expected. This work aims to investigate why. An agent-based simulation is developed in order to model the fishers as intelligent agents with complex interaction. Fishermen’s decision making is simulated through an artificial neural network which adapts its behavior (i.e. weights) by “learning-by-imitation”, a method in evolutionary game theory, from other stakeholders’ behavior in the environment. The promising results show that with further development the simulation system may be part of a decision support system that promotes policies that are fair for the stakeholders.
Debate sites in social media provide a unified platform for citizens to discuss controversial que... more Debate sites in social media provide a unified platform for citizens to discuss controversial questions and to put forward their ideas and arguments on the issues of common interest. Opinions of citizens may provide useful knowledge to stakeholders but manual analysis of arguments in debate sites is tedious, while computational support to this end has been rather scarce. We focus here on developing a technical instrumentation for making sense of a set of online arguments and aggregating them into usable results for policy making and climate science communication. Our objectives are: (i) to aggregate arguments posted for a certain debate topic, (ii) to consolidate opinions posted under several but related topics either in the same or different debate site, and (iii) to identify possible linguistic characteristics of the argumentative texts. For the first objective, we propose a voting method based on subjective logic [13]. For the second objective, we assess the semantic similarity between two debate topics based on textual entailment [28]. For the third objective, we employ various existing methods for lexical analysis such as frequency analysis or readability indexes. Although we focused here on the climate change, the method can be applied to any domain.
This paper discusses the need to recognize and take advantage of arising opportunities when an ag... more This paper discusses the need to recognize and take advantage of arising opportunities when an agent is autonomously interacting with an environment. We discuss the tasks, methods, model framework, and use it to analyze opportunities across several dimensions: when changing the tasks versus changing the methods, when having a complete knowledge about the methods in the domain versus having partial knowledge, when having a complete model of the domain versus having an incomplete model, when tackling a single task versus multiple tasks and finally we discuss the role of causal explanations in opportunistic case-based decision making.
International Journal of Human Computer Studies, Mar 1, 1998
Decision-support systems that help solving problems in open and weak theory domains, i.e. hard pr... more Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.
International Journal of Human Computer Studies, Mar 1, 1998
Decision-support systems that help solving problems in open and weak theory domains, i.e. hard pr... more Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.
Case Based Reasoning(CBR), an artificial intelligence technique, solves new problem by reusing so... more Case Based Reasoning(CBR), an artificial intelligence technique, solves new problem by reusing solutions of previously solved similar cases. In conventional CBR, cases are represented in terms of structured attribute-value pairs. Acquisition of cases, either from domain experts or through manually crafting attribute-value pairs from incident reports, constitutes the main reason why CBR systems have not been more common in industries. Manual case generation is a laborious, costlier and time consuming task. Textual CBR (TCBR) is an emerging line that aims to apply CBR techniques on cases represented as textual descriptions. Similarity of cases is based on the similarity between their constituting features. Conventional CBR benefits from employing domain specific knowledge for similarity assessment. Correspondingly, TCBR needs to involve higher-order relationships between features, hence domain specific knowledge. In addition, the term order has also been contended to influence the similarity assessment. This paper presents an account where features and cases are represented using a distributed representation paradigm that captures higherorder relations among features as well as term order information.
This paper presents a collaborative agents model for Sales and Operation Planning (S&OP) in indus... more This paper presents a collaborative agents model for Sales and Operation Planning (S&OP) in industry. We show how an S&OP system can be used in a multi-agent approach considering existing legacy software and how people and software agents interact in a planning environment in a process industry, such as the petrochemical industry. The model helps to describe the process, determine the pre-existing software integrations, define responsibilities, and promote communication and learning. The solution adopted is crucial for a high performance S&OP. It is also important to consider the interaction among human agents and software agents, which are required for its success. The multi-agent system (MAS) paradigm is useful for helping industries handle distributed information sources and interactions between a number of actors and teams. We examine activities of an S&OP in a petrochemical company and how these activities can be more efficiently performed through MAS. 1 Introduction Many research studies have focused on the adoption of multi-agent systems (MASs) in industrial environments [1, 2]. Inside manufacturing systems, MASs provide solutions for manufacturing enterprise integration [3], enterprise collaboration [4], manufacturing process planning and scheduling [5, 6], shop floor control and holonic manufacturing [7]. In the production planning and control field, MASs are applied to order quotations, demand forecasts, order management, purchasing, master production scheduling, monitoring and distribution [8]. In all of these examples, MASs are useful for data collection, data analysis, communication and information exchange. At the tactical level of production planning in Sales and Operation Planning (S&OP) [9], these agents help in the reduction of planning time, activities coordination, control information and retrieve learned-lessons that are relevant for computer reasoning. S&OP is a business process that links the corporate strategic plan to daily operation plans [10]. It includes representatives from operations, supply chain,
One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant feat... more One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant features. A solution to this problem is to assign weights to features that indicate their salience for classification. Current weighting methods can be divided as global weighting, partial local weighting, and local weighting methods enumerated in increasing order of capability to capture the features' relative salience in classification. However, the existing methods are not sensitive enough to describe the salience of a feature and can be changed given different queries. We suggest that the salience of a feature, in addition to being sensitive to the instance (i.e. varies across instances), should also be sensitive to the variations in the difference of a feature's values between a query and the instances in the instance base. In this paper, we put forward a dynamic feature weighting approach which has more expressive capability, and present a sketch of a classification algorithm based on the notion of dynamic weights.
Background: Patients with diabetes use an increasing number of self-management tools in their dai... more Background: Patients with diabetes use an increasing number of self-management tools in their daily life. However, health institutions rarely use the data generated by these services mainly due to (1) the lack of data reliability, and (2) medical workers spending too much time extracting relevant information from the vast amount of data produced. This work is part of the FullFlow project, which focuses on self-collected health data sharing directly between patients' tools and EHRs.
This paper presents an architecture for an intelligent virtual agent that imitates human drumming... more This paper presents an architecture for an intelligent virtual agent that imitates human drumming behaviour. Through imitation, the agent models the user-specific variations that constitute the "groove" of the drummer. The architecture comprises a motor system that imitates arm movements of a human drummer, and a sound system that produces the sound of the human playing style. The presence of a sound system alleviates the need to use physical models that will create sound when a drum is struck, instead focusing on creating an imitative agent that booth looks and sounds similar to its teacher. Such a virtual agent can be used in a musical setting, where its visualization and sound system would allow it to be regarded as an artificial musician. The architecture is implemented using Echo State Networks, and relies on self-organization and a bottom-up approach when learning human drum patterns.
Today's organizations, under increasing pressure on the effectiveness and the increasing need for... more Today's organizations, under increasing pressure on the effectiveness and the increasing need for dealing with complex tasks beyond a single individual's capabilities , need technological support in managing complex tasks that involve highly distributed and heterogeneous information sources and several actors. This paper describes CoPSF, a multiagent system middle-ware that simplifies the development of coordinated problem solving applications while ensuring standard compliance through a set of system services and agents. CoPSF hosts and serves multiple concurrent teams of problem solving contributing both to the limitation of communication overheads and to the reduction of redundant work across teams and organizations. The framework employs (i) an interleaved task decomposition and allocation approach, (ii) a mechanism for coordination of agents' work, and (iii) a mechanism that enables synergy between parallel teams.
In order for us to trust artificial intelligence systems, they need to be able to explain their d... more In order for us to trust artificial intelligence systems, they need to be able to explain their decisions. This article investigates a case-based reasoning (CBR) approach for generating explanations where a new event is explained by combining and modifying explanations of multiple previous events. This approach is implemented in a CBR system for incident analysis where the goal is to identify causes of transportation incidents. The system generates explanations that connect the observed events to the root causes of the incident through intermediate states and events. Explanations for past incidents are automatically extracted from textual incident reports using natural language processing, thus avoiding the manual effort of constructing explanations. The system is evaluated on incident reports from the Transportation Safety Board of Canada, supporting the hypothesis that adapting explanations of multiple previous events rather than a single event results in a more comprehensive explanation of a new event.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics , 2004
One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant feat... more One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant features. A solution to this problem is to assign weights to features that indicate their salience for classification. Current weighting methods can be divided as global weighting, partial local weighting, and local weighting methods enumerated in increasing order of capability to capture the features' relative salience in classification. However, the existing methods are not sensitive enough to describe the salience of a feature and can be changed given different queries. We suggest that the salience of a feature, in addition to being sensitive to the instance (i.e. varies across instances), should also be sensitive to the variations in the difference of a feature's values between a query and the instances in the instance base. In this paper, we put forward a dynamic feature weighting approach which has more expressive capability, and present a sketch of a classification algorithm based on the notion of dynamic weights. 0-7803-8403-2104/920.00 @2004 IEEE 2406
When problem solving systems are deployed in real life, it is usually not enough to provide only ... more When problem solving systems are deployed in real life, it is usually not enough to provide only a solution without any explanation. Users need an explanation in order to trust the system's decisions. At the same time, explanations may also function internally in the system's own reasoning process. One way to come up with an explanation for a new problem is to adapt an explanation from a similar problem encountered earlier, which is the idea behind the case-based explanation approach introduced by [29]. The original approach relies on manual construction of cases with explanations, which is difficult to scale up. In earlier work, therefore, we developed a system for automatic acquisition of cases with explanations from textual reports, including retrieval and adaptation of such cases [33,32]. In this paper, we improve the adaptation method by combining explanations from more than one case, which we call com-positional adaptation. The method is evaluated on an incident analysis task where the goal is to identify the root causes of a transportation incident , explaining it in terms of the information contained in the incident description. The evaluation results show that the proposed approach increases both the recall and the precision of the system.
Aquaculture expansion is a political priority in Norway, despite simmering conflicts and competin... more Aquaculture expansion is a political priority in Norway, despite simmering conflicts and competing claims. We expand on this hypothesis and analyze the Norwegian governance system by adding stakeholder theory in case of a simulated model of the effects of municipal coastal zone planning in the municipality of Frøya, Norway. One cannot analyze the governance system in Norway without fully comprehending the perspectives of the stakeholders involved. Different stakeholders will react and respond differently and have conflicting presumptions basing their actions towards the planning process for coastal areas. They will also have different levels of power and abilities to influence the system. The article presents the interdisciplinary, first generation development of an agent based simulation model that mimics the outcomes of coastal zone planning for a stakeholder groups, the commercial fishers and the aquaculture industry, based on qualitative input from legislation, regulations and stakeholder workshops. We proceed with verifying the applicability of this simulator in light of the key actors involved, namely the commercial fishers. We found that the simulator had two outcomes for the commercial fishers that were consistently recurring, namely "collapse" and "stability", based on the simulated occurrences of complaints by the stakeholders, with the latter being the de facto perceptions of actuality by the commercial fishers. Using stakeholder theory, we argue that the aquaculture industry’s role has the saliency of an Important Stakeholder in Frøya has steered the commercial fishers, who has the role of Dependent Stakeholders according to stakeholder theory, to no longer see any legitimacy in the process in that their complaints were never upheld because of their lack of the attribute Power.
The paper presents a computational analysis of the results from a sorting task with motion verbs ... more The paper presents a computational analysis of the results from a sorting task with motion verbs in Norwegian. The sorting behavior of humans rests on the features they use when they compare two or more words. We investigate what these features are and how differential each feature may be in sorting.
2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2013
Load peaks can have a negative impact on the stability of the power grid and maintenance costs fo... more Load peaks can have a negative impact on the stability of the power grid and maintenance costs for transmission and generation companies. Currently, increasing use of plug-in hybrid electric vehicles (PHEV) further proliferates the problem because charging patterns are expected to coincide with peak demand hours, especially the afternoon peak hours.
Floods are among the most destructive natural disasters, which are highly complex to model. The r... more Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literatur...
Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attem... more Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility. INDEX TERMS Case-based reasoning, data minimization, data privacy, data silos, decision support systems, federated architecture, federated case-based reasoning.
Aquaculture organizations establish facilities at the coast in Froya, Norway. The facilities bloc... more Aquaculture organizations establish facilities at the coast in Froya, Norway. The facilities block the surrounding area from fishing and cause environmental damage to close natural resources. Fishers who depend on those natural resources get the opportunity to influence the aquaculture expansion through complaints about the municipality’s coastal plan. Statistics show that fishers don’t complain as much as expected. This work aims to investigate why. An agent-based simulation is developed in order to model the fishers as intelligent agents with complex interaction. Fishermen’s decision making is simulated through an artificial neural network which adapts its behavior (i.e. weights) by “learning-by-imitation”, a method in evolutionary game theory, from other stakeholders’ behavior in the environment. The promising results show that with further development the simulation system may be part of a decision support system that promotes policies that are fair for the stakeholders.
Debate sites in social media provide a unified platform for citizens to discuss controversial que... more Debate sites in social media provide a unified platform for citizens to discuss controversial questions and to put forward their ideas and arguments on the issues of common interest. Opinions of citizens may provide useful knowledge to stakeholders but manual analysis of arguments in debate sites is tedious, while computational support to this end has been rather scarce. We focus here on developing a technical instrumentation for making sense of a set of online arguments and aggregating them into usable results for policy making and climate science communication. Our objectives are: (i) to aggregate arguments posted for a certain debate topic, (ii) to consolidate opinions posted under several but related topics either in the same or different debate site, and (iii) to identify possible linguistic characteristics of the argumentative texts. For the first objective, we propose a voting method based on subjective logic [13]. For the second objective, we assess the semantic similarity between two debate topics based on textual entailment [28]. For the third objective, we employ various existing methods for lexical analysis such as frequency analysis or readability indexes. Although we focused here on the climate change, the method can be applied to any domain.
This paper discusses the need to recognize and take advantage of arising opportunities when an ag... more This paper discusses the need to recognize and take advantage of arising opportunities when an agent is autonomously interacting with an environment. We discuss the tasks, methods, model framework, and use it to analyze opportunities across several dimensions: when changing the tasks versus changing the methods, when having a complete knowledge about the methods in the domain versus having partial knowledge, when having a complete model of the domain versus having an incomplete model, when tackling a single task versus multiple tasks and finally we discuss the role of causal explanations in opportunistic case-based decision making.
International Journal of Human Computer Studies, Mar 1, 1998
Decision-support systems that help solving problems in open and weak theory domains, i.e. hard pr... more Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.
International Journal of Human Computer Studies, Mar 1, 1998
Decision-support systems that help solving problems in open and weak theory domains, i.e. hard pr... more Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.
Case Based Reasoning(CBR), an artificial intelligence technique, solves new problem by reusing so... more Case Based Reasoning(CBR), an artificial intelligence technique, solves new problem by reusing solutions of previously solved similar cases. In conventional CBR, cases are represented in terms of structured attribute-value pairs. Acquisition of cases, either from domain experts or through manually crafting attribute-value pairs from incident reports, constitutes the main reason why CBR systems have not been more common in industries. Manual case generation is a laborious, costlier and time consuming task. Textual CBR (TCBR) is an emerging line that aims to apply CBR techniques on cases represented as textual descriptions. Similarity of cases is based on the similarity between their constituting features. Conventional CBR benefits from employing domain specific knowledge for similarity assessment. Correspondingly, TCBR needs to involve higher-order relationships between features, hence domain specific knowledge. In addition, the term order has also been contended to influence the similarity assessment. This paper presents an account where features and cases are represented using a distributed representation paradigm that captures higherorder relations among features as well as term order information.
This paper presents a collaborative agents model for Sales and Operation Planning (S&OP) in indus... more This paper presents a collaborative agents model for Sales and Operation Planning (S&OP) in industry. We show how an S&OP system can be used in a multi-agent approach considering existing legacy software and how people and software agents interact in a planning environment in a process industry, such as the petrochemical industry. The model helps to describe the process, determine the pre-existing software integrations, define responsibilities, and promote communication and learning. The solution adopted is crucial for a high performance S&OP. It is also important to consider the interaction among human agents and software agents, which are required for its success. The multi-agent system (MAS) paradigm is useful for helping industries handle distributed information sources and interactions between a number of actors and teams. We examine activities of an S&OP in a petrochemical company and how these activities can be more efficiently performed through MAS. 1 Introduction Many research studies have focused on the adoption of multi-agent systems (MASs) in industrial environments [1, 2]. Inside manufacturing systems, MASs provide solutions for manufacturing enterprise integration [3], enterprise collaboration [4], manufacturing process planning and scheduling [5, 6], shop floor control and holonic manufacturing [7]. In the production planning and control field, MASs are applied to order quotations, demand forecasts, order management, purchasing, master production scheduling, monitoring and distribution [8]. In all of these examples, MASs are useful for data collection, data analysis, communication and information exchange. At the tactical level of production planning in Sales and Operation Planning (S&OP) [9], these agents help in the reduction of planning time, activities coordination, control information and retrieve learned-lessons that are relevant for computer reasoning. S&OP is a business process that links the corporate strategic plan to daily operation plans [10]. It includes representatives from operations, supply chain,
One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant feat... more One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant features. A solution to this problem is to assign weights to features that indicate their salience for classification. Current weighting methods can be divided as global weighting, partial local weighting, and local weighting methods enumerated in increasing order of capability to capture the features' relative salience in classification. However, the existing methods are not sensitive enough to describe the salience of a feature and can be changed given different queries. We suggest that the salience of a feature, in addition to being sensitive to the instance (i.e. varies across instances), should also be sensitive to the variations in the difference of a feature's values between a query and the instances in the instance base. In this paper, we put forward a dynamic feature weighting approach which has more expressive capability, and present a sketch of a classification algorithm based on the notion of dynamic weights.
Background: Patients with diabetes use an increasing number of self-management tools in their dai... more Background: Patients with diabetes use an increasing number of self-management tools in their daily life. However, health institutions rarely use the data generated by these services mainly due to (1) the lack of data reliability, and (2) medical workers spending too much time extracting relevant information from the vast amount of data produced. This work is part of the FullFlow project, which focuses on self-collected health data sharing directly between patients' tools and EHRs.
This paper presents an architecture for an intelligent virtual agent that imitates human drumming... more This paper presents an architecture for an intelligent virtual agent that imitates human drumming behaviour. Through imitation, the agent models the user-specific variations that constitute the "groove" of the drummer. The architecture comprises a motor system that imitates arm movements of a human drummer, and a sound system that produces the sound of the human playing style. The presence of a sound system alleviates the need to use physical models that will create sound when a drum is struck, instead focusing on creating an imitative agent that booth looks and sounds similar to its teacher. Such a virtual agent can be used in a musical setting, where its visualization and sound system would allow it to be regarded as an artificial musician. The architecture is implemented using Echo State Networks, and relies on self-organization and a bottom-up approach when learning human drum patterns.
Today's organizations, under increasing pressure on the effectiveness and the increasing need for... more Today's organizations, under increasing pressure on the effectiveness and the increasing need for dealing with complex tasks beyond a single individual's capabilities , need technological support in managing complex tasks that involve highly distributed and heterogeneous information sources and several actors. This paper describes CoPSF, a multiagent system middle-ware that simplifies the development of coordinated problem solving applications while ensuring standard compliance through a set of system services and agents. CoPSF hosts and serves multiple concurrent teams of problem solving contributing both to the limitation of communication overheads and to the reduction of redundant work across teams and organizations. The framework employs (i) an interleaved task decomposition and allocation approach, (ii) a mechanism for coordination of agents' work, and (iii) a mechanism that enables synergy between parallel teams.
In order for us to trust artificial intelligence systems, they need to be able to explain their d... more In order for us to trust artificial intelligence systems, they need to be able to explain their decisions. This article investigates a case-based reasoning (CBR) approach for generating explanations where a new event is explained by combining and modifying explanations of multiple previous events. This approach is implemented in a CBR system for incident analysis where the goal is to identify causes of transportation incidents. The system generates explanations that connect the observed events to the root causes of the incident through intermediate states and events. Explanations for past incidents are automatically extracted from textual incident reports using natural language processing, thus avoiding the manual effort of constructing explanations. The system is evaluated on incident reports from the Transportation Safety Board of Canada, supporting the hypothesis that adapting explanations of multiple previous events rather than a single event results in a more comprehensive explanation of a new event.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics , 2004
One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant feat... more One major problem of nearest neighbor (NN) algorithms is inefficiency incurred by irrelevant features. A solution to this problem is to assign weights to features that indicate their salience for classification. Current weighting methods can be divided as global weighting, partial local weighting, and local weighting methods enumerated in increasing order of capability to capture the features' relative salience in classification. However, the existing methods are not sensitive enough to describe the salience of a feature and can be changed given different queries. We suggest that the salience of a feature, in addition to being sensitive to the instance (i.e. varies across instances), should also be sensitive to the variations in the difference of a feature's values between a query and the instances in the instance base. In this paper, we put forward a dynamic feature weighting approach which has more expressive capability, and present a sketch of a classification algorithm based on the notion of dynamic weights. 0-7803-8403-2104/920.00 @2004 IEEE 2406
When problem solving systems are deployed in real life, it is usually not enough to provide only ... more When problem solving systems are deployed in real life, it is usually not enough to provide only a solution without any explanation. Users need an explanation in order to trust the system's decisions. At the same time, explanations may also function internally in the system's own reasoning process. One way to come up with an explanation for a new problem is to adapt an explanation from a similar problem encountered earlier, which is the idea behind the case-based explanation approach introduced by [29]. The original approach relies on manual construction of cases with explanations, which is difficult to scale up. In earlier work, therefore, we developed a system for automatic acquisition of cases with explanations from textual reports, including retrieval and adaptation of such cases [33,32]. In this paper, we improve the adaptation method by combining explanations from more than one case, which we call com-positional adaptation. The method is evaluated on an incident analysis task where the goal is to identify the root causes of a transportation incident , explaining it in terms of the information contained in the incident description. The evaluation results show that the proposed approach increases both the recall and the precision of the system.
Aquaculture expansion is a political priority in Norway, despite simmering conflicts and competin... more Aquaculture expansion is a political priority in Norway, despite simmering conflicts and competing claims. We expand on this hypothesis and analyze the Norwegian governance system by adding stakeholder theory in case of a simulated model of the effects of municipal coastal zone planning in the municipality of Frøya, Norway. One cannot analyze the governance system in Norway without fully comprehending the perspectives of the stakeholders involved. Different stakeholders will react and respond differently and have conflicting presumptions basing their actions towards the planning process for coastal areas. They will also have different levels of power and abilities to influence the system. The article presents the interdisciplinary, first generation development of an agent based simulation model that mimics the outcomes of coastal zone planning for a stakeholder groups, the commercial fishers and the aquaculture industry, based on qualitative input from legislation, regulations and stakeholder workshops. We proceed with verifying the applicability of this simulator in light of the key actors involved, namely the commercial fishers. We found that the simulator had two outcomes for the commercial fishers that were consistently recurring, namely "collapse" and "stability", based on the simulated occurrences of complaints by the stakeholders, with the latter being the de facto perceptions of actuality by the commercial fishers. Using stakeholder theory, we argue that the aquaculture industry’s role has the saliency of an Important Stakeholder in Frøya has steered the commercial fishers, who has the role of Dependent Stakeholders according to stakeholder theory, to no longer see any legitimacy in the process in that their complaints were never upheld because of their lack of the attribute Power.
The paper presents a computational analysis of the results from a sorting task with motion verbs ... more The paper presents a computational analysis of the results from a sorting task with motion verbs in Norwegian. The sorting behavior of humans rests on the features they use when they compare two or more words. We investigate what these features are and how differential each feature may be in sorting.
2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2013
Load peaks can have a negative impact on the stability of the power grid and maintenance costs fo... more Load peaks can have a negative impact on the stability of the power grid and maintenance costs for transmission and generation companies. Currently, increasing use of plug-in hybrid electric vehicles (PHEV) further proliferates the problem because charging patterns are expected to coincide with peak demand hours, especially the afternoon peak hours.
Uploads
Papers by pinar ozturk