Interests
Main.Interests History
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January 20, 2015, at 03:14 PM
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Recently, I have become more interested in hybrid man-machine collective intelligence, with two focal areas: social computation, and the evolution of meaning. In social computation, I am interested in how large collectives of collaborating humans and software agents can solve problems on the Web that are too complex for humans or machines to solve by themselves. In research on the evolution of meaning, I am investigating how artificial agents can benefit from the mechanisms humans use in negotiating the meaning of what they communicate with each other in order to improve the interoperability of distributed systems, but also to understand the social foundation of collective intelligence.''
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Recently, I have become more interested in hybrid man-machine collective intelligence, with two focal areas: social computation, and the evolution of meaning. In social computation, I am interested in how large collectives of collaborating humans and software agents can solve problems on the Web that are too complex for humans or machines to solve by themselves. In research on the evolution of meaning, I am investigating how artificial agents can benefit from the mechanisms humans use in negotiating the meaning of what they communicate with each other in order to improve the interoperability of distributed systems, but also to understand the social foundation of collective intelligence''
January 20, 2015, at 03:13 PM
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January 20, 2015, at 03:13 PM
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'''Michael Rovatsos'''
'+Multiagent systems and social computation.+'
''For the past 15 years, my research has been in multiagent systems, where most of my contributions have been in agent communication languages, multiagent planning and learning, trust and reputation, social reasoning, argumentation, and norms. Most of this work has focused on reasoning about interaction, more specifically the development of adaptive and scalable knowledge-based methods for reaching agreement, resolving conflict, and sharing knowledge among agents. My work uses mainly methods from symbolic AI such as automated planning, deductive reasoning and declarative approaches to systems modelling, but it has also involved such techniques as reinforcement learning and statistical reasoning. While I am interested in building rigorous models of intelligent reasoning and decision making, my research emphasises the importance of producing systems that work in practice.
Recently, I have become more interested in hybrid man-machine collective intelligence, with two focal areas: social computation, and the evolution of meaning. In social computation, I am interested in how large collectives of collaborating humans and software agents can solve problems on the Web that are too complex for humans or machines to solve by themselves. In research on the evolution of meaning, I am investigating how artificial agents can benefit from the mechanisms humans use in negotiating the meaning of what they communicate with each other in order to improve the interoperability of distributed systems, but also to understand the social foundation of collective intelligence.''
http://homepages.inf.ed.ac.uk/mrovatso
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'+Multiagent systems and social computation.+'
''For the past 15 years, my research has been in multiagent systems, where most of my contributions have been in agent communication languages, multiagent planning and learning, trust and reputation, social reasoning, argumentation, and norms. Most of this work has focused on reasoning about interaction, more specifically the development of adaptive and scalable knowledge-based methods for reaching agreement, resolving conflict, and sharing knowledge among agents. My work uses mainly methods from symbolic AI such as automated planning, deductive reasoning and declarative approaches to systems modelling, but it has also involved such techniques as reinforcement learning and statistical reasoning. While I am interested in building rigorous models of intelligent reasoning and decision making, my research emphasises the importance of producing systems that work in practice.
Recently, I have become more interested in hybrid man-machine collective intelligence, with two focal areas: social computation, and the evolution of meaning. In social computation, I am interested in how large collectives of collaborating humans and software agents can solve problems on the Web that are too complex for humans or machines to solve by themselves. In research on the evolution of meaning, I am investigating how artificial agents can benefit from the mechanisms humans use in negotiating the meaning of what they communicate with each other in order to improve the interoperability of distributed systems, but also to understand the social foundation of collective intelligence.''
http://homepages.inf.ed.ac.uk/mrovatso
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March 27, 2014, at 04:31 PM
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'+''Computational Nudging or How to alter a Human Agent's (HA) policy by controlling the information presented.''+'
Utilizing a HA's learned preference model, we can guide his choice by adjusting the representation of alternatives in the feature space. Moreover, the presented alternatives can alter his perception of the feasibility of a future incentive, thus controlling its effectiveness.
Utilizing a HA's learned preference model, we can guide his choice by adjusting the representation of alternatives in the feature space. Moreover, the presented alternatives can alter his perception of the feasibility of a future incentive, thus controlling its effectiveness.
to:
'+Computational Nudging or How to alter a Human Agent's (HA) policy by controlling the information presented.+'
''Utilizing a HA's learned preference model, we can guide his choice by adjusting the representation of alternatives in the feature space. Moreover, the presented alternatives can alter his perception of the feasibility of a future incentive, thus controlling its effectiveness.''
''Utilizing a HA's learned preference model, we can guide his choice by adjusting the representation of alternatives in the feature space. Moreover, the presented alternatives can alter his perception of the feasibility of a future incentive, thus controlling its effectiveness.''
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'+''Semantic Alignment of Heterogeneous Ontologies Induced from Sensor Data''+'
My research is concerned with ontology alignment between heterogeneous ontologies where each ontology is induced independently from sensor measurements. We consider each disparate sensor/sensor cluster as an agent and all agents as a collaborative multi-agent system. As ontologies model a particular conceptualisation of a domain, for agents to exchange information about domain they must align ontologies. We are interested in the case that sharing domain knowledge improves a joint task performed by all agents and evidence for alignment takes the form of ontology samples exchanged between agents as messages in a coordinated communication game.
My research is concerned with ontology alignment between heterogeneous ontologies where each ontology is induced independently from sensor measurements. We consider each disparate sensor/sensor cluster as an agent and all agents as a collaborative multi-agent system. As ontologies model a particular conceptualisation of a domain, for agents to exchange information about domain they must align ontologies. We are interested in the case that sharing domain knowledge improves a joint task performed by all agents and evidence for alignment takes the form of ontology samples exchanged between agents as messages in a coordinated communication game.
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'+Semantic Alignment of Heterogeneous Ontologies Induced from Sensor Data+'
''My research is concerned with ontology alignment between heterogeneous ontologies where each ontology is induced independently from sensor measurements. We consider each disparate sensor/sensor cluster as an agent and all agents as a collaborative multi-agent system. As ontologies model a particular conceptualisation of a domain, for agents to exchange information about domain they must align ontologies. We are interested in the case that sharing domain knowledge improves a joint task performed by all agents and evidence for alignment takes the form of ontology samples exchanged between agents as messages in a coordinated communication game.''
''My research is concerned with ontology alignment between heterogeneous ontologies where each ontology is induced independently from sensor measurements. We consider each disparate sensor/sensor cluster as an agent and all agents as a collaborative multi-agent system. As ontologies model a particular conceptualisation of a domain, for agents to exchange information about domain they must align ontologies. We are interested in the case that sharing domain knowledge improves a joint task performed by all agents and evidence for alignment takes the form of ontology samples exchanged between agents as messages in a coordinated communication game.''
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'+''Compositionality and social orchestration in the Smart Society project and the Smart Sharing application''+'
Dimitris worked for his PhD on the analysis of algorithms in different variants of the Probably Approximately Correct model of learning (evolvability, multiple-instance learning, active learning), as well as on question answering with the aid of the commonsense knowledge base ConceptNet 4. Dimitris is currently working under the workpackage for compositionality and social orchestration in the Smart Society project and develops in parallel the relevant backend functionality of the server for the prototype of Smart Sharing.
Dimitris worked for his PhD on the analysis of algorithms in different variants of the Probably Approximately Correct model of learning (evolvability, multiple-instance learning, active learning), as well as on question answering with the aid of the commonsense knowledge base ConceptNet 4. Dimitris is currently working under the workpackage for compositionality and social orchestration in the Smart Society project and develops in parallel the relevant backend functionality of the server for the prototype of Smart Sharing.
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'+Compositionality and social orchestration in the Smart Society project and the Smart Sharing application+'
''Dimitris worked for his PhD on the analysis of algorithms in different variants of the Probably Approximately Correct model of learning (evolvability, multiple-instance learning, active learning), as well as on question answering with the aid of the commonsense knowledge base ConceptNet 4. Dimitris is currently working under the workpackage for compositionality and social orchestration in the Smart Society project and develops in parallel the relevant backend functionality of the server for the prototype of Smart Sharing.''
''Dimitris worked for his PhD on the analysis of algorithms in different variants of the Probably Approximately Correct model of learning (evolvability, multiple-instance learning, active learning), as well as on question answering with the aid of the commonsense knowledge base ConceptNet 4. Dimitris is currently working under the workpackage for compositionality and social orchestration in the Smart Society project and develops in parallel the relevant backend functionality of the server for the prototype of Smart Sharing.''
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'+''Content Creators and the Semantic Web''+'
Behind the plethora of user-generated media content on the web are a multitude of content creators, actively pushing their creative works to a global audience. The activities, needs and desires of these content creators transcend the individual websites and communities they use. In order to answer questions about identity behaviours and social network dynamics on a large scale, as well as to build applications that can facilitate collaboration between content creators, I am working to incorporate these creators and their works into the linked data cloud.
Behind the plethora of user-generated media content on the web are a multitude of content creators, actively pushing their creative works to a global audience. The activities, needs and desires of these content creators transcend the individual websites and communities they use. In order to answer questions about identity behaviours and social network dynamics on a large scale, as well as to build applications that can facilitate collaboration between content creators, I am working to incorporate these creators and their works into the linked data cloud.
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'+Content Creators and the Semantic Web+'
''Behind the plethora of user-generated media content on the web are a multitude of content creators, actively pushing their creative works to a global audience. The activities, needs and desires of these content creators transcend the individual websites and communities they use. In order to answer questions about identity behaviours and social network dynamics on a large scale, as well as to build applications that can facilitate collaboration between content creators, I am working to incorporate these creators and their works into the linked data cloud.''
''Behind the plethora of user-generated media content on the web are a multitude of content creators, actively pushing their creative works to a global audience. The activities, needs and desires of these content creators transcend the individual websites and communities they use. In order to answer questions about identity behaviours and social network dynamics on a large scale, as well as to build applications that can facilitate collaboration between content creators, I am working to incorporate these creators and their works into the linked data cloud.''
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'' Communication Planning''
Humans are not sole entities but social beings that can accomplish more working together than by themselves. This is only possible because humans are able to communicate and agree on what to do together. In the "Communication Planning" project (WP3/ESSENCE), we are interested in modelling this behaviour for autonomous and independent agents in a way that they will be able to communicate with a different agent or human without knowing their internal structure. In a few words, given a complex task environment, what kind of planning activity should an agent do to decide what should it say to whom in order to accomplish its goals.
Humans are not sole entities but social beings that can accomplish more working together than by themselves. This is only possible because humans are able to communicate and agree on what to do together. In the "Communication Planning" project (WP3/ESSENCE), we are interested in modelling this behaviour for autonomous and independent agents in a way that they will be able to communicate with a different agent or human without knowing their internal structure. In a few words, given a complex task environment, what kind of planning activity should an agent do to decide what should it say to whom in order to accomplish its goals.
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'+Communication Planning+'
''Humans are not sole entities but social beings that can accomplish more working together than by themselves. This is only possible because humans are able to communicate and agree on what to do together. In the "Communication Planning" project (WP3/ESSENCE), we are interested in modelling this behaviour for autonomous and independent agents in a way that they will be able to communicate with a different agent or human without knowing their internal structure. In a few words, given a complex task environment, what kind of planning activity should an agent do to decide what should it say to whom in order to accomplish its goals.''
''Humans are not sole entities but social beings that can accomplish more working together than by themselves. This is only possible because humans are able to communicate and agree on what to do together. In the "Communication Planning" project (WP3/ESSENCE), we are interested in modelling this behaviour for autonomous and independent agents in a way that they will be able to communicate with a different agent or human without knowing their internal structure. In a few words, given a complex task environment, what kind of planning activity should an agent do to decide what should it say to whom in order to accomplish its goals.''
March 27, 2014, at 04:29 PM
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Key Words: Incentives, behavioural modelling, recommender systems, human Behaviour modelling (learning preferences, designing Incentives, and nudging)
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Interests: Incentives, behavioural modelling, recommender systems, human Behaviour modelling (learning preferences, designing Incentives, and nudging)
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Language Games, Ontology Grounding, Ontology Matching, Ontology Negotiation
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Interests: Language Games, Ontology Grounding, Ontology Matching, Ontology Negotiation
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Keywords: semantic web, user-generated content, content creators, linked data, linked media, social network analysis, social machines.
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Interests: semantic web, user-generated content, content creators, linked data, linked media, social network analysis, social machines.
March 27, 2014, at 04:27 PM
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''Computational Nudging or How to alter a Human Agent's (HA) policy by controlling the information presented.''
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'+''Computational Nudging or How to alter a Human Agent's (HA) policy by controlling the information presented.''+'
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Key Words: Incentives, behavioural modelling, recommender systems, human Behaviour modelling (learning preferences, designing Incentives, and nudging)
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''Semantic Alignment of Heterogeneous Ontologies Induced from Sensor Data''
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'+''Semantic Alignment of Heterogeneous Ontologies Induced from Sensor Data''+'
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''Compositionality and social orchestration in the Smart Society project and the Smart Sharing application''
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'+''Compositionality and social orchestration in the Smart Society project and the Smart Sharing application''+'
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''Content Creators and the Semantic Web''
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'+''Content Creators and the Semantic Web''+'
March 27, 2014, at 04:24 PM
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March 27, 2014, at 04:22 PM
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!!!Compositionality and social orchestration in the Smart Society project and the Smart Sharing application
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''Compositionality and social orchestration in the Smart Society project and the Smart Sharing application''
March 27, 2014, at 04:21 PM
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''Compositionality and social orchestration in the Smart Society project and the Smart Sharing application''
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!!!Compositionality and social orchestration in the Smart Society project and the Smart Sharing application
March 27, 2014, at 04:19 PM
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March 27, 2014, at 04:09 PM
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||Andreadis, Pavlos || queueing theory, machine learning || incentives, behavioural modelling ||
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||Andreadis, Pavlos || queueing theory, machine learning || incentives, behavioural modelling, recommender systems, human Behaviour modelling (learning preferences, designing Incentives, and nudging) ||
February 26, 2014, at 03:40 PM
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||Marques, Tania || TBA || TBA ||
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February 10, 2014, at 10:14 PM
by - Added Tania and Nicola to the list
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||Bova, Nicola || TBA || TBA ||
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||Marques, Tania || TBA || TBA ||
January 27, 2014, at 09:49 PM
by - added interests for Sergio
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||Elizondo, Sergio || TBA || TBA ||
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||Elizondo, Sergio || market-based prosumer coordination for future electricity networks || complex adaptive systems, game theory ||
January 27, 2014, at 01:06 AM
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||Elizondo, Sergio || TBA || TBA ||
January 14, 2014, at 04:32 PM
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||Anslow, Michael || symbolic grounding, language games, ontologies || sensor nets, machine learning, cognitive science ||
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||Anslow, Michael || symbolic grounding, language games, ontologies || sensor nets, sensor fusion, machine learning, cognitive science ||
January 14, 2014, at 01:52 PM
by - Minor editing so that non-names and jargon are in lowercase letters
Changed lines 4-12 from:
||! Name ||! Expertise ||! Broader Interests ||
||Andreadis, Pavlos || Queueing theory, machine learning || Incentives, behavioural modelling ||
||Anslow, Michael || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Diochnos, Dimitris || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Guhe, Markus || Cognitive modelling, natural language dialogue || Cognitive science ||
||Guy, Amy || Semantic web, social informatics || Network analysis, provenance ||
||Lascarides, Alex || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Manataki, Areti || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Motta, Gustavo || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Andreadis, Pavlos || Queueing theory, machine learning || Incentives, behavioural modelling ||
||Anslow, Michael || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Diochnos, Dimitris || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Guhe, Markus || Cognitive modelling, natural language dialogue || Cognitive science ||
||Guy, Amy || Semantic web, social informatics || Network analysis, provenance ||
||Lascarides, Alex || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Manataki, Areti || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Motta, Gustavo || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
to:
||! name ||! expertise ||! broader interests ||
||Andreadis, Pavlos || queueing theory, machine learning || incentives, behavioural modelling ||
||Anslow, Michael || symbolic grounding, language games, ontologies || sensor nets, machine learning, cognitive science ||
||Diochnos, Dimitris || learning, reasoning (question-answering) || knowledge bases, network analysis, evolution ||
||Guhe, Markus || cognitive modelling, natural language dialogue || cognitive science ||
||Guy, Amy || semantic web, social informatics || network analysis, provenance ||
||Lascarides, Alex || natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Manataki, Areti || workflow modelling, simulation analysis (distributed systems) || knowledge base systems, argumentation and application areas ||
||Motta, Gustavo || distributed systems, security on health informatics || tele-radiology and new methods, complex adaptive systems ||
||Andreadis, Pavlos || queueing theory, machine learning || incentives, behavioural modelling ||
||Anslow, Michael || symbolic grounding, language games, ontologies || sensor nets, machine learning, cognitive science ||
||Diochnos, Dimitris || learning, reasoning (question-answering) || knowledge bases, network analysis, evolution ||
||Guhe, Markus || cognitive modelling, natural language dialogue || cognitive science ||
||Guy, Amy || semantic web, social informatics || network analysis, provenance ||
||Lascarides, Alex || natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Manataki, Areti || workflow modelling, simulation analysis (distributed systems) || knowledge base systems, argumentation and application areas ||
||Motta, Gustavo || distributed systems, security on health informatics || tele-radiology and new methods, complex adaptive systems ||
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||Papapanagiotou, Petros || Process modelling and formal verification || Concurrent systems, semantic web ||
||Queiroz, Natasha || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Queiroz, Natasha || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
to:
||Papapanagiotou, Petros || process modelling and formal verification || concurrent systems, semantic web ||
||Queiroz, Natasha || planning applications on knowledge bases, semantic web concepts || health informatics, collaborative systems ||
||Queiroz, Natasha || planning applications on knowledge bases, semantic web concepts || health informatics, collaborative systems ||
Changed line 17 from:
||Yang, Guoli || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||
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||Yang, Guoli || RUAOE modelling (symbolic) || network dynamics, evolutionary game theory ||
January 14, 2014, at 01:46 PM
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||Andreadis, Pavlos || Queueing theory, machine learning || Incentives, behavioural medelling ||
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||Andreadis, Pavlos || Queueing theory, machine learning || Incentives, behavioural modelling ||
January 14, 2014, at 01:45 PM
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Changed lines 5-17 from:
||Pavlos Andreadis || Queueing theory, machine learning || Incentives, behavioural medelling ||
||Michael Anslow || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Dimitris Diochnos || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Markus Guhe || Cognitive modelling, natural language dialogue || Cognitive science ||
||Amy Guy || Semantic web, social informatics || Network analysis, provenance ||
||Alex Lascarides || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Areti Manataki || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Gustavo Motta || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Dave Murray-Rust || TBA || TBA ||
||Petros Papapanagiotou || Process modelling and formal verification || Concurrent systems, semantic web ||
||Natasha Queiroz || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Michael Rovatsos || multiagent systems, especially planning, communication and learning || social computation, negotiation and evolution of meaning ||
||Guoli Yang || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||
||Michael Anslow || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Dimitris Diochnos || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Markus Guhe || Cognitive modelling, natural language dialogue || Cognitive science ||
||Amy Guy || Semantic web, social informatics || Network analysis, provenance ||
||Alex Lascarides || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Areti Manataki || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Gustavo Motta || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Dave Murray-Rust || TBA || TBA ||
||Petros Papapanagiotou || Process modelling and formal verification || Concurrent systems, semantic web ||
||Natasha Queiroz || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Michael Rovatsos || multiagent systems, especially planning, communication and learning || social computation, negotiation and evolution of meaning ||
||Guoli Yang || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||
to:
||Andreadis, Pavlos || Queueing theory, machine learning || Incentives, behavioural medelling ||
||Anslow, Michael || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Diochnos, Dimitris || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Guhe, Markus || Cognitive modelling, natural language dialogue || Cognitive science ||
||Guy, Amy || Semantic web, social informatics || Network analysis, provenance ||
||Lascarides, Alex || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Manataki, Areti || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Motta, Gustavo || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Murray-Rust, Dave || TBA || TBA ||
||Papapanagiotou, Petros || Process modelling and formal verification || Concurrent systems, semantic web ||
||Queiroz, Natasha || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Rovatsos, Michael || multiagent systems, especially planning, communication and learning || social computation, negotiation and evolution of meaning ||
||Yang, Guoli || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||
||Anslow, Michael || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Diochnos, Dimitris || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Guhe, Markus || Cognitive modelling, natural language dialogue || Cognitive science ||
||Guy, Amy || Semantic web, social informatics || Network analysis, provenance ||
||Lascarides, Alex || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Manataki, Areti || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Motta, Gustavo || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Murray-Rust, Dave || TBA || TBA ||
||Papapanagiotou, Petros || Process modelling and formal verification || Concurrent systems, semantic web ||
||Queiroz, Natasha || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Rovatsos, Michael || multiagent systems, especially planning, communication and learning || social computation, negotiation and evolution of meaning ||
||Yang, Guoli || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||
January 14, 2014, at 01:41 PM
by - Added information with peoples' interests
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The people who are attending regularly the seminar have the following areas of expertise and broader interests.
|| border=1 width=100%
||! Name ||! Expertise ||! Broader Interests ||
||Pavlos Andreadis || Queueing theory, machine learning || Incentives, behavioural medelling ||
||Michael Anslow || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Dimitris Diochnos || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Markus Guhe || Cognitive modelling, natural language dialogue || Cognitive science ||
||Amy Guy || Semantic web, social informatics || Network analysis, provenance ||
||Alex Lascarides || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Areti Manataki || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Gustavo Motta || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Dave Murray-Rust || TBA || TBA ||
||Petros Papapanagiotou || Process modelling and formal verification || Concurrent systems, semantic web ||
||Natasha Queiroz || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Michael Rovatsos || multiagent systems, especially planning, communication and learning || social computation, negotiation and evolution of meaning ||
||Guoli Yang || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||
|| border=1 width=100%
||! Name ||! Expertise ||! Broader Interests ||
||Pavlos Andreadis || Queueing theory, machine learning || Incentives, behavioural medelling ||
||Michael Anslow || Symbolic grounding, language games, ontologies || Sensor nets, machine learning, cognitive science ||
||Dimitris Diochnos || Learning, reasoning (question-answering) || Knowledge bases, network analysis, evolution ||
||Markus Guhe || Cognitive modelling, natural language dialogue || Cognitive science ||
||Amy Guy || Semantic web, social informatics || Network analysis, provenance ||
||Alex Lascarides || Natural language semantics, commonsense reasoning, games in extended form, CP-Nets || Interaction and human action and decision making ||
||Areti Manataki || Workflow modelling, simulation analysis (distributed systems) || Knowledge base systems, argumentation and application areas ||
||Gustavo Motta || Distributed systems, security on health informatics || Tele-radiology and new methods, complex adaptive systems ||
||Dave Murray-Rust || TBA || TBA ||
||Petros Papapanagiotou || Process modelling and formal verification || Concurrent systems, semantic web ||
||Natasha Queiroz || Planning applications on knowledge bases, semantic web concepts || Health informatics, collaborative systems ||
||Michael Rovatsos || multiagent systems, especially planning, communication and learning || social computation, negotiation and evolution of meaning ||
||Guoli Yang || RUAOE modelling (symbolic) || Network dynamics, evolutionary game theory ||