A guided tour of artificial intelligence research: volume 1 Knowledge representation, reasoning and learning
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Datensatz im Suchindex
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| adam_text | Contents Elements for a History of Artificial Intelligence.......................................... Pierre Marquis, Odile Papini and Henri Prade 1 Knowledge Representation: Modalities, Conditionals, and Nonmonotonic Reasoning......................................................................... Andreas Herzig and Philippe Besnard 45 Representations of Uncertainty in Artificial Intelligence: Probability and Possibility................................................................................ Thierry Denœux, Didier Dubois and Henri Prade 69 Representations of Uncertainty in AI: Beyond Probability and Possibility..................................................................................................... Thierry Denœux, Didier Dubois and Henri Prade 119 Qualitative Reasoning ....................................................................................... Jean-François Condotta, Florence Le Ber, Gerard Ligozat and Louise Travé-Massuyès 151 Reasoning with Ontologies................................................................................ Meghyn Bienvenu, Michel Leclère, Marie-Laure Mugnier and Marie-Christine Rousset 185 Compact Representation of Preferences........................................................ Souhila Касі, Jérôme Lang and Patrice Pemy 217 Norms and Deontic Logic.................................................................................. Frédéric Cuppens, Christophe Garion, Guillaume Piolie and Nora Cuppens-Boulahia 253 A Glance at Causality Theories forArtificial Intelligence......................... Didier Dubois and
Henri Prade 275 ix
x Contents Case-Based Reasoning, Analogy, and Interpolation.................................... Béatrice Fuchs, Jean Lieber, Laurent Mielet, Alain Mille, Amedeo Napoli, Henri Prade and Gilles Richard 307 Statistical Computational Learning................................................................ Antoine Comuejols, Frédéric Koriche and Richard Nock 341 Reinforcement Learning..................................................................................... Olivier Buffet, Olivier Pietquin and Paul Weng 389 Argumentation and Inconsistency-Tolerant Reasoning............................... Leila Amgoud, Philippe Besnard, Claudette Cayrol, Philippe Chatalic and Marie-Christine Lagasquie-Schiex 415 Main Issues in Belief Revision, Belief Merging and Information Fusion..................................................................................... Didier Dubois, Patricia Everaere, Sébastien Konieczny and Odile Papini 441 Reasoning About Action and Change.............................................................. Florence Dupin de Saint-Cyr, Andreas Herzig, Jéròme Lang and Pierre Marquis 487 Multicriteria Decision Making......................................................................... Christophe Gonzales and Patrice Pemy 519 Decision Under Uncertainty.............................................................................. Christophe Gonzales and Patrice Pemy 549 Collective Decision Making.............................................................................. Sylvain Bouveret, Jéròme Lang and Michel Lemaître 587 Formalization of Cognitive-Agent
Systems, Trust, and Emotions............ Jonathan Ben-Naim, Dominique Longin and Emiliano Lorini 629 Negotiation and Persuasion Among Agents.................................................. Leila Amgoud, Yann Chevaleyre and Nicolas Maudet 651 Diagnosis and Supervision: Model-Based Approaches............................... Marie-Odile Cordier, Philippe Dague, Yannick Pencóle and Louise Travé-Massuyès 673 Validation and Explanation.............................................................................. Laurent Chamay, Juliette Dibie and Stéphane Loiseau 707 Knowledge Engineering..................................................................................... Nathalie Aussenac-Gilles, Jean Charlet and Chantal Reynaud 733 Afterword - From Formal Reasoning to Trust............................................. Luis Fariñas del Cerro 769 Index 773
Pierre Marquis · Odile Papini · Henri Prade Editors A Guided Tour of Artificial Intelligence Research Ί Knowledge Representation, Reasoning and Learning The purpose of this book is to pnrvide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: • • • the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI algorithms) the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. The volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust
and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, rea soning based on similarity (case-based, interpolative, or analogical), as well as reason ing about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statisti cal computational learning and reinforcement learning (other machine learning top ics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
Contents Elements for a History of Artificial Intelligence.......................................... Pierre Marquis, Odile Papini and Henri Prade 1 Knowledge Representation: Modalities, Conditionals, and Nonmonotonic Reasoning......................................................................... Andreas Herzig and Philippe Besnard 45 Representations of Uncertainty in Artificial Intelligence: Probability and Possibility................................................................................ Thierry Denœux, Didier Dubois and Henri Prade 69 Representations of Uncertainty in AI: Beyond Probability and Possibility..................................................................................................... Thierry Denœux, Didier Dubois and Henri Prade 119 Qualitative Reasoning ....................................................................................... Jean-François Condotta, Florence Le Ber, Gerard Ligozat and Louise Travé-Massuyès 151 Reasoning with Ontologies................................................................................ Meghyn Bienvenu, Michel Leclère, Marie-Laure Mugnier and Marie-Christine Rousset 185 Compact Representation of Preferences........................................................ Souhila Касі, Jérôme Lang and Patrice Pemy 217 Norms and Deontic Logic.................................................................................. Frédéric Cuppens, Christophe Garion, Guillaume Piolie and Nora Cuppens-Boulahia 253 A Glance at Causality Theories forArtificial Intelligence......................... Didier Dubois and
Henri Prade 275 ix
x Contents Case-Based Reasoning, Analogy, and Interpolation.................................... Béatrice Fuchs, Jean Lieber, Laurent Mielet, Alain Mille, Amedeo Napoli, Henri Prade and Gilles Richard 307 Statistical Computational Learning................................................................ Antoine Comuejols, Frédéric Koriche and Richard Nock 341 Reinforcement Learning..................................................................................... Olivier Buffet, Olivier Pietquin and Paul Weng 389 Argumentation and Inconsistency-Tolerant Reasoning............................... Leila Amgoud, Philippe Besnard, Claudette Cayrol, Philippe Chatalic and Marie-Christine Lagasquie-Schiex 415 Main Issues in Belief Revision, Belief Merging and Information Fusion..................................................................................... Didier Dubois, Patricia Everaere, Sébastien Konieczny and Odile Papini 441 Reasoning About Action and Change.............................................................. Florence Dupin de Saint-Cyr, Andreas Herzig, Jéròme Lang and Pierre Marquis 487 Multicriteria Decision Making......................................................................... Christophe Gonzales and Patrice Pemy 519 Decision Under Uncertainty.............................................................................. Christophe Gonzales and Patrice Pemy 549 Collective Decision Making.............................................................................. Sylvain Bouveret, Jéròme Lang and Michel Lemaître 587 Formalization of Cognitive-Agent
Systems, Trust, and Emotions............ Jonathan Ben-Naim, Dominique Longin and Emiliano Lorini 629 Negotiation and Persuasion Among Agents.................................................. Leila Amgoud, Yann Chevaleyre and Nicolas Maudet 651 Diagnosis and Supervision: Model-Based Approaches............................... Marie-Odile Cordier, Philippe Dague, Yannick Pencóle and Louise Travé-Massuyès 673 Validation and Explanation.............................................................................. Laurent Chamay, Juliette Dibie and Stéphane Loiseau 707 Knowledge Engineering..................................................................................... Nathalie Aussenac-Gilles, Jean Charlet and Chantal Reynaud 733 Afterword - From Formal Reasoning to Trust............................................. Luis Fariñas del Cerro 769 Index 773
Pierre Marquis · Odile Papini · Henri Prade Editors A Guided Tour of Artificial Intelligence Research Ί Knowledge Representation, Reasoning and Learning The purpose of this book is to pnrvide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: • • • the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI algorithms) the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. The volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust
and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, rea soning based on similarity (case-based, interpolative, or analogical), as well as reason ing about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statisti cal computational learning and reinforcement learning (other machine learning top ics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
Contents Elements for a History of Artificial Intelligence.......................................... Pierre Marquis, Odile Papini and Henri Prade 1 Knowledge Representation: Modalities, Conditionals, and Nonmonotonic Reasoning......................................................................... Andreas Herzig and Philippe Besnard 45 Representations of Uncertainty in Artificial Intelligence: Probability and Possibility................................................................................ Thierry Denœux, Didier Dubois and Henri Prade 69 Representations of Uncertainty in AI: Beyond Probability and Possibility..................................................................................................... Thierry Denœux, Didier Dubois and Henri Prade 119 Qualitative Reasoning ....................................................................................... Jean-François Condotta, Florence Le Ber, Gerard Ligozat and Louise Travé-Massuyès 151 Reasoning with Ontologies................................................................................ Meghyn Bienvenu, Michel Leclère, Marie-Laure Mugnier and Marie-Christine Rousset 185 Compact Representation of Preferences........................................................ Souhila Касі, Jérôme Lang and Patrice Pemy 217 Norms and Deontic Logic.................................................................................. Frédéric Cuppens, Christophe Garion, Guillaume Piolie and Nora Cuppens-Boulahia 253 A Glance at Causality Theories forArtificial Intelligence......................... Didier Dubois and
Henri Prade 275 ix
x Contents Case-Based Reasoning, Analogy, and Interpolation.................................... Béatrice Fuchs, Jean Lieber, Laurent Mielet, Alain Mille, Amedeo Napoli, Henri Prade and Gilles Richard 307 Statistical Computational Learning................................................................ Antoine Comuejols, Frédéric Koriche and Richard Nock 341 Reinforcement Learning..................................................................................... Olivier Buffet, Olivier Pietquin and Paul Weng 389 Argumentation and Inconsistency-Tolerant Reasoning............................... Leila Amgoud, Philippe Besnard, Claudette Cayrol, Philippe Chatalic and Marie-Christine Lagasquie-Schiex 415 Main Issues in Belief Revision, Belief Merging and Information Fusion..................................................................................... Didier Dubois, Patricia Everaere, Sébastien Konieczny and Odile Papini 441 Reasoning About Action and Change.............................................................. Florence Dupin de Saint-Cyr, Andreas Herzig, Jéròme Lang and Pierre Marquis 487 Multicriteria Decision Making......................................................................... Christophe Gonzales and Patrice Pemy 519 Decision Under Uncertainty.............................................................................. Christophe Gonzales and Patrice Pemy 549 Collective Decision Making.............................................................................. Sylvain Bouveret, Jéròme Lang and Michel Lemaître 587 Formalization of Cognitive-Agent
Systems, Trust, and Emotions............ Jonathan Ben-Naim, Dominique Longin and Emiliano Lorini 629 Negotiation and Persuasion Among Agents.................................................. Leila Amgoud, Yann Chevaleyre and Nicolas Maudet 651 Diagnosis and Supervision: Model-Based Approaches............................... Marie-Odile Cordier, Philippe Dague, Yannick Pencóle and Louise Travé-Massuyès 673 Validation and Explanation.............................................................................. Laurent Chamay, Juliette Dibie and Stéphane Loiseau 707 Knowledge Engineering..................................................................................... Nathalie Aussenac-Gilles, Jean Charlet and Chantal Reynaud 733 Afterword - From Formal Reasoning to Trust............................................. Luis Fariñas del Cerro 769 Index 773
Pierre Marquis · Odile Papini · Henri Prade Editors A Guided Tour of Artificial Intelligence Research Ί Knowledge Representation, Reasoning and Learning The purpose of this book is to pnrvide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: • • • the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI algorithms) the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. The volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust
and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, rea soning based on similarity (case-based, interpolative, or analogical), as well as reason ing about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statisti cal computational learning and reinforcement learning (other machine learning top ics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
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Contents Elements for a History of Artificial Intelligence. Pierre Marquis, Odile Papini and Henri Prade 1 Knowledge Representation: Modalities, Conditionals, and Nonmonotonic Reasoning. Andreas Herzig and Philippe Besnard 45 Representations of Uncertainty in Artificial Intelligence: Probability and Possibility. Thierry Denœux, Didier Dubois and Henri Prade 69 Representations of Uncertainty in AI: Beyond Probability and Possibility. Thierry Denœux, Didier Dubois and Henri Prade 119 Qualitative Reasoning . Jean-François Condotta, Florence Le Ber, Gerard Ligozat and Louise Travé-Massuyès 151 Reasoning with Ontologies. Meghyn Bienvenu, Michel Leclère, Marie-Laure Mugnier and Marie-Christine Rousset 185 Compact Representation of Preferences. Souhila Касі, Jérôme Lang and Patrice Pemy 217 Norms and Deontic Logic. Frédéric Cuppens, Christophe Garion, Guillaume Piolie and Nora Cuppens-Boulahia 253 A Glance at Causality Theories forArtificial Intelligence. Didier Dubois and
Henri Prade 275 ix
x Contents Case-Based Reasoning, Analogy, and Interpolation. Béatrice Fuchs, Jean Lieber, Laurent Mielet, Alain Mille, Amedeo Napoli, Henri Prade and Gilles Richard 307 Statistical Computational Learning. Antoine Comuejols, Frédéric Koriche and Richard Nock 341 Reinforcement Learning. Olivier Buffet, Olivier Pietquin and Paul Weng 389 Argumentation and Inconsistency-Tolerant Reasoning. Leila Amgoud, Philippe Besnard, Claudette Cayrol, Philippe Chatalic and Marie-Christine Lagasquie-Schiex 415 Main Issues in Belief Revision, Belief Merging and Information Fusion. Didier Dubois, Patricia Everaere, Sébastien Konieczny and Odile Papini 441 Reasoning About Action and Change. Florence Dupin de Saint-Cyr, Andreas Herzig, Jéròme Lang and Pierre Marquis 487 Multicriteria Decision Making. Christophe Gonzales and Patrice Pemy 519 Decision Under Uncertainty. Christophe Gonzales and Patrice Pemy 549 Collective Decision Making. Sylvain Bouveret, Jéròme Lang and Michel Lemaître 587 Formalization of Cognitive-Agent
Systems, Trust, and Emotions. Jonathan Ben-Naim, Dominique Longin and Emiliano Lorini 629 Negotiation and Persuasion Among Agents. Leila Amgoud, Yann Chevaleyre and Nicolas Maudet 651 Diagnosis and Supervision: Model-Based Approaches. Marie-Odile Cordier, Philippe Dague, Yannick Pencóle and Louise Travé-Massuyès 673 Validation and Explanation. Laurent Chamay, Juliette Dibie and Stéphane Loiseau 707 Knowledge Engineering. Nathalie Aussenac-Gilles, Jean Charlet and Chantal Reynaud 733 Afterword - From Formal Reasoning to Trust. Luis Fariñas del Cerro 769 Index 773
Pierre Marquis · Odile Papini · Henri Prade Editors A Guided Tour of Artificial Intelligence Research Ί Knowledge Representation, Reasoning and Learning The purpose of this book is to pnrvide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: • • • the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI algorithms) the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. The volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust
and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, rea soning based on similarity (case-based, interpolative, or analogical), as well as reason ing about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statisti cal computational learning and reinforcement learning (other machine learning top ics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
Contents Elements for a History of Artificial Intelligence. Pierre Marquis, Odile Papini and Henri Prade 1 Knowledge Representation: Modalities, Conditionals, and Nonmonotonic Reasoning. Andreas Herzig and Philippe Besnard 45 Representations of Uncertainty in Artificial Intelligence: Probability and Possibility. Thierry Denœux, Didier Dubois and Henri Prade 69 Representations of Uncertainty in AI: Beyond Probability and Possibility. Thierry Denœux, Didier Dubois and Henri Prade 119 Qualitative Reasoning . Jean-François Condotta, Florence Le Ber, Gerard Ligozat and Louise Travé-Massuyès 151 Reasoning with Ontologies. Meghyn Bienvenu, Michel Leclère, Marie-Laure Mugnier and Marie-Christine Rousset 185 Compact Representation of Preferences. Souhila Касі, Jérôme Lang and Patrice Pemy 217 Norms and Deontic Logic. Frédéric Cuppens, Christophe Garion, Guillaume Piolie and Nora Cuppens-Boulahia 253 A Glance at Causality Theories forArtificial Intelligence. Didier Dubois and
Henri Prade 275 ix
x Contents Case-Based Reasoning, Analogy, and Interpolation. Béatrice Fuchs, Jean Lieber, Laurent Mielet, Alain Mille, Amedeo Napoli, Henri Prade and Gilles Richard 307 Statistical Computational Learning. Antoine Comuejols, Frédéric Koriche and Richard Nock 341 Reinforcement Learning. Olivier Buffet, Olivier Pietquin and Paul Weng 389 Argumentation and Inconsistency-Tolerant Reasoning. Leila Amgoud, Philippe Besnard, Claudette Cayrol, Philippe Chatalic and Marie-Christine Lagasquie-Schiex 415 Main Issues in Belief Revision, Belief Merging and Information Fusion. Didier Dubois, Patricia Everaere, Sébastien Konieczny and Odile Papini 441 Reasoning About Action and Change. Florence Dupin de Saint-Cyr, Andreas Herzig, Jéròme Lang and Pierre Marquis 487 Multicriteria Decision Making. Christophe Gonzales and Patrice Pemy 519 Decision Under Uncertainty. Christophe Gonzales and Patrice Pemy 549 Collective Decision Making. Sylvain Bouveret, Jéròme Lang and Michel Lemaître 587 Formalization of Cognitive-Agent
Systems, Trust, and Emotions. Jonathan Ben-Naim, Dominique Longin and Emiliano Lorini 629 Negotiation and Persuasion Among Agents. Leila Amgoud, Yann Chevaleyre and Nicolas Maudet 651 Diagnosis and Supervision: Model-Based Approaches. Marie-Odile Cordier, Philippe Dague, Yannick Pencóle and Louise Travé-Massuyès 673 Validation and Explanation. Laurent Chamay, Juliette Dibie and Stéphane Loiseau 707 Knowledge Engineering. Nathalie Aussenac-Gilles, Jean Charlet and Chantal Reynaud 733 Afterword - From Formal Reasoning to Trust. Luis Fariñas del Cerro 769 Index 773
Pierre Marquis · Odile Papini · Henri Prade Editors A Guided Tour of Artificial Intelligence Research Ί Knowledge Representation, Reasoning and Learning The purpose of this book is to pnrvide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: • • • the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI algorithms) the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. The volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust
and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, rea soning based on similarity (case-based, interpolative, or analogical), as well as reason ing about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statisti cal computational learning and reinforcement learning (other machine learning top ics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
Contents Elements for a History of Artificial Intelligence. Pierre Marquis, Odile Papini and Henri Prade 1 Knowledge Representation: Modalities, Conditionals, and Nonmonotonic Reasoning. Andreas Herzig and Philippe Besnard 45 Representations of Uncertainty in Artificial Intelligence: Probability and Possibility. Thierry Denœux, Didier Dubois and Henri Prade 69 Representations of Uncertainty in AI: Beyond Probability and Possibility. Thierry Denœux, Didier Dubois and Henri Prade 119 Qualitative Reasoning . Jean-François Condotta, Florence Le Ber, Gerard Ligozat and Louise Travé-Massuyès 151 Reasoning with Ontologies. Meghyn Bienvenu, Michel Leclère, Marie-Laure Mugnier and Marie-Christine Rousset 185 Compact Representation of Preferences. Souhila Касі, Jérôme Lang and Patrice Pemy 217 Norms and Deontic Logic. Frédéric Cuppens, Christophe Garion, Guillaume Piolie and Nora Cuppens-Boulahia 253 A Glance at Causality Theories forArtificial Intelligence. Didier Dubois and
Henri Prade 275 ix
x Contents Case-Based Reasoning, Analogy, and Interpolation. Béatrice Fuchs, Jean Lieber, Laurent Mielet, Alain Mille, Amedeo Napoli, Henri Prade and Gilles Richard 307 Statistical Computational Learning. Antoine Comuejols, Frédéric Koriche and Richard Nock 341 Reinforcement Learning. Olivier Buffet, Olivier Pietquin and Paul Weng 389 Argumentation and Inconsistency-Tolerant Reasoning. Leila Amgoud, Philippe Besnard, Claudette Cayrol, Philippe Chatalic and Marie-Christine Lagasquie-Schiex 415 Main Issues in Belief Revision, Belief Merging and Information Fusion. Didier Dubois, Patricia Everaere, Sébastien Konieczny and Odile Papini 441 Reasoning About Action and Change. Florence Dupin de Saint-Cyr, Andreas Herzig, Jéròme Lang and Pierre Marquis 487 Multicriteria Decision Making. Christophe Gonzales and Patrice Pemy 519 Decision Under Uncertainty. Christophe Gonzales and Patrice Pemy 549 Collective Decision Making. Sylvain Bouveret, Jéròme Lang and Michel Lemaître 587 Formalization of Cognitive-Agent
Systems, Trust, and Emotions. Jonathan Ben-Naim, Dominique Longin and Emiliano Lorini 629 Negotiation and Persuasion Among Agents. Leila Amgoud, Yann Chevaleyre and Nicolas Maudet 651 Diagnosis and Supervision: Model-Based Approaches. Marie-Odile Cordier, Philippe Dague, Yannick Pencóle and Louise Travé-Massuyès 673 Validation and Explanation. Laurent Chamay, Juliette Dibie and Stéphane Loiseau 707 Knowledge Engineering. Nathalie Aussenac-Gilles, Jean Charlet and Chantal Reynaud 733 Afterword - From Formal Reasoning to Trust. Luis Fariñas del Cerro 769 Index 773
Pierre Marquis · Odile Papini · Henri Prade Editors A Guided Tour of Artificial Intelligence Research Ί Knowledge Representation, Reasoning and Learning The purpose of this book is to pnrvide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: • • • the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI algorithms) the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. The volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust
and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, rea soning based on similarity (case-based, interpolative, or analogical), as well as reason ing about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statisti cal computational learning and reinforcement learning (other machine learning top ics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume. |
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| spelling | A guided tour of artificial intelligence research volume 1 Knowledge representation, reasoning and learning Pierre Marquis, Odile Papini, Henri Prade (editors) Cham Springer [2020] xv, 803 Seiten Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Lernverhalten (DE-588)4167413-3 gnd rswk-swf Logisches Denken (DE-588)4323319-3 gnd rswk-swf Wissensrepräsentation (DE-588)4049534-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Wissensrepräsentation (DE-588)4049534-6 s Logisches Denken (DE-588)4323319-3 s Lernverhalten (DE-588)4167413-3 s DE-604 Marquis, Pierre (DE-588)138224048 edt Papini, Odile edt Prade, Henri 1953- (DE-588)170518280 edt (DE-604)BV046915800 1 Erscheint auch als Online-Ausgabe 978-3-030-06164-7 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032325177&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032325177&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032325177&sequence=000005&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032325177&sequence=000007&line_number=0004&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032325177&sequence=000009&line_number=0005&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032325177&sequence=000011&line_number=0006&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | A guided tour of artificial intelligence research Lernverhalten (DE-588)4167413-3 gnd Logisches Denken (DE-588)4323319-3 gnd Wissensrepräsentation (DE-588)4049534-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
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| title | A guided tour of artificial intelligence research |
| title_auth | A guided tour of artificial intelligence research |
| title_exact_search | A guided tour of artificial intelligence research |
| title_exact_search_txtP | A guided tour of artificial intelligence research |
| title_full | A guided tour of artificial intelligence research volume 1 Knowledge representation, reasoning and learning Pierre Marquis, Odile Papini, Henri Prade (editors) |
| title_fullStr | A guided tour of artificial intelligence research volume 1 Knowledge representation, reasoning and learning Pierre Marquis, Odile Papini, Henri Prade (editors) |
| title_full_unstemmed | A guided tour of artificial intelligence research volume 1 Knowledge representation, reasoning and learning Pierre Marquis, Odile Papini, Henri Prade (editors) |
| title_short | A guided tour of artificial intelligence research |
| title_sort | a guided tour of artificial intelligence research knowledge representation reasoning and learning |
| topic | Lernverhalten (DE-588)4167413-3 gnd Logisches Denken (DE-588)4323319-3 gnd Wissensrepräsentation (DE-588)4049534-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Lernverhalten Logisches Denken Wissensrepräsentation Künstliche Intelligenz Aufsatzsammlung |
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