Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Computational logic in multi-agent systems : 7th international workshop, CLIMA VII, Hakodate, Japan, May 8-9, 2006 : revised selected and invited papers / / Katsumi Inoue, Ken Satoh, Francesca Toni (editors)
| Computational logic in multi-agent systems : 7th international workshop, CLIMA VII, Hakodate, Japan, May 8-9, 2006 : revised selected and invited papers / / Katsumi Inoue, Ken Satoh, Francesca Toni (editors) |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (X, 318 p.) |
| Disciplina | 004.015113 |
| Collana | Lecture notes in artificial intelligence |
| Soggetto topico | Computer logic |
| ISBN |
1-280-86404-4
9786610864041 3-540-69619-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Regular Papers -- Acts of Commanding and Changing Obligations -- Hierarchical Decision Making in Multi-agent Systems Using Answer Set Programming -- On a Linear Framework for Belief Dynamics in Multi-agent Environments -- Answer Set Programming for Representing and Reasoning About Virtual Institutions -- A Complete Probabilistic Belief Logic -- Prototyping 3APL in the Maude Term Rewriting Language -- Dialogue Game Tree with Nondeterministic Additive Consolidation -- Representing and Verifying Temporal Epistemic Properties in Multi-Agent Systems -- A New Logical Semantics for Agent Communication -- Contextual Reasoning in Agent Systems -- An Argumentation-Based Negotiation for Distributed Extended Logic Programs -- Belief Updating by Communication Channel -- On the Implementation of Global Abduction -- Adding Evolving Abilities to a Multi-Agent System -- Contest Papers -- The Second Contest on Multi-Agent Systems Based on Computational Logic -- Using Antimodels to Define Agents’ Strategy -- Multi-Agent FLUX for the Gold Mining Domain (System Description) -- Using Jason to Implement a Team of Gold Miners. |
| Record Nr. | UNINA-9910484635303321 |
| Berlin ; ; Heidelberg : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational logic in multi-agent systems : 7th international workshop, CLIMA VII, Hakodate, Japan, May 8-9, 2006 : revised selected and invited papers / / Katsumi Inoue, Ken Satoh, Francesca Toni (editors)
| Computational logic in multi-agent systems : 7th international workshop, CLIMA VII, Hakodate, Japan, May 8-9, 2006 : revised selected and invited papers / / Katsumi Inoue, Ken Satoh, Francesca Toni (editors) |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (X, 318 p.) |
| Disciplina | 004.015113 |
| Collana | Lecture notes in artificial intelligence |
| Soggetto topico | Computer logic |
| ISBN |
1-280-86404-4
9786610864041 3-540-69619-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Regular Papers -- Acts of Commanding and Changing Obligations -- Hierarchical Decision Making in Multi-agent Systems Using Answer Set Programming -- On a Linear Framework for Belief Dynamics in Multi-agent Environments -- Answer Set Programming for Representing and Reasoning About Virtual Institutions -- A Complete Probabilistic Belief Logic -- Prototyping 3APL in the Maude Term Rewriting Language -- Dialogue Game Tree with Nondeterministic Additive Consolidation -- Representing and Verifying Temporal Epistemic Properties in Multi-Agent Systems -- A New Logical Semantics for Agent Communication -- Contextual Reasoning in Agent Systems -- An Argumentation-Based Negotiation for Distributed Extended Logic Programs -- Belief Updating by Communication Channel -- On the Implementation of Global Abduction -- Adding Evolving Abilities to a Multi-Agent System -- Contest Papers -- The Second Contest on Multi-Agent Systems Based on Computational Logic -- Using Antimodels to Define Agents’ Strategy -- Multi-Agent FLUX for the Gold Mining Domain (System Description) -- Using Jason to Implement a Team of Gold Miners. |
| Record Nr. | UNISA-996465871603316 |
| Berlin ; ; Heidelberg : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
| Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 215 p. 56 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Data mining Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Data Mining and Knowledge Discovery |
| ISBN | 3-319-40566-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References.
Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996466006103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
| Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 215 p. 56 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Data mining Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Data Mining and Knowledge Discovery |
| ISBN | 3-319-40566-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References.
Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910482960503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Logical modeling of biological systems / / edited by Luis Fariñas del Cerro, Katsumi Inoue
| Logical modeling of biological systems / / edited by Luis Fariñas del Cerro, Katsumi Inoue |
| Pubbl/distr/stampa | London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (429 p.) |
| Disciplina | 570.28 |
| Collana | Bioengineering and Health Science Series |
| Soggetto topico |
Biology - Methodology
Biology - Philosophy Evolution (Biology) |
| ISBN |
1-119-01521-9
1-119-00522-1 1-119-01533-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Foreword; Chapter 1. Symbolic Representation and Inference of Regulatory Network Structures; 1.1. Introduction: logical modeling and abductive inference in systems biology; 1.2. Logical modeling of regulatory networks; 1.2.1. Background; 1.2.2. Logical model of signed-directed networks; 1.2.2.1. Prior knowledge; 1.2.2.2. Rule-based underlying model; 1.2.2.3. Integrity constraints; 1.2.2.4. Inferring signed-directed networks and explanatory reasoning; 1.3. Evaluation of the ARNI approach; 1.3.1. ARNI predictive power
1.3.1.1. Prediction under biological and experimental noise1.3.1.2. Prediction under incomplete data; 1.3.2. ARNI expressive power; 1.3.2.1. Network motif representations; 1.3.2.2. Representing complex interactions; 1.4. ARNI assisted scientific methodology; 1.4.1. Testing biological hypotheses; 1.4.1.1. Testing cross-talk between signaling pathways; 1.4.2. Informative experiments for networks discrimination; 1.5. Related work and comparison with non-symbolic approaches; 1.5.1. Limitations and future work; 1.6. Conclusions; 1.7. Bibliography Chapter 2. Reasoning on the Response of Logical Signaling Networks with ASP2.1. Introduction; 2.2. Answer set programming at a glance; 2.3. Learn and control logical networks with ASP; 2.3.1. Preliminaries; 2.3.2. Reasoning on the response of logical networks; 2.3.3. Learning models of immediate-early response; 2.3.4. Minimal intervention strategies; 2.3.5. Software toolbox: caspo; 2.4. Conclusion; 2.5. Acknowledgments; 2.6. Bibliography; Chapter 3. A Logical Model for Molecular Interaction Maps; 3.1. Introduction; 3.2. Biological background; 3.3. Logical model 3.3.1. Activation and inhibition3.3.1.1. Activation and inhibition capacities; 3.3.1.2. Relations between the activation and inhibition causes and effects; 3.3.1.3. Relations between causal relations; 3.3.2. Model extension; 3.3.2.1. Phosphorylation; 3.3.2.2. Autophosphorylation; 3.3.2.3. Binding; 3.3.3. Causality relations redefinition; 3.3.3.1. Activation axioms; 3.3.3.2. Phosphorylation axioms; 3.3.3.3. Autophosphorylation axioms; 3.3.3.4. Binding axioms; 3.3.3.5. Inhibition axioms; 3.4. Quantifier elimination for restricted formulas; 3.4.1. Domain formulas; 3.4.2. Restricted formulas 3.4.3. Completion formulas3.4.4. Domain of domain formulas; 3.4.5. Quantifier elimination procedure; 3.5. Reasoning about interactions in metabolic interaction maps; 3.6. Conclusion and future work; 3.7. Acknowledgments; 3.8. Bibliography; Chapter 4. Analyzing Large Network Dynamics with Process Hitting; 4.1. Introduction/state of the art; 4.1.1. The modeling challenge; 4.1.2. Historical context: Boolean and discrete models; 4.1.3. Analysis issues; 4.1.4. The process hitting framework; 4.1.5. Outline; 4.2. Discrete modeling with the process hitting; 4.2.1. Motivation 4.2.2. The process hitting framework |
| Record Nr. | UNINA-9910132156603321 |
| London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Logical modeling of biological systems / / edited by Luis Fariñas del Cerro, Katsumi Inoue
| Logical modeling of biological systems / / edited by Luis Fariñas del Cerro, Katsumi Inoue |
| Pubbl/distr/stampa | London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (429 p.) |
| Disciplina | 570.28 |
| Collana | Bioengineering and Health Science Series |
| Soggetto topico |
Biology - Methodology
Biology - Philosophy Evolution (Biology) |
| ISBN |
1-119-01521-9
1-119-00522-1 1-119-01533-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Foreword; Chapter 1. Symbolic Representation and Inference of Regulatory Network Structures; 1.1. Introduction: logical modeling and abductive inference in systems biology; 1.2. Logical modeling of regulatory networks; 1.2.1. Background; 1.2.2. Logical model of signed-directed networks; 1.2.2.1. Prior knowledge; 1.2.2.2. Rule-based underlying model; 1.2.2.3. Integrity constraints; 1.2.2.4. Inferring signed-directed networks and explanatory reasoning; 1.3. Evaluation of the ARNI approach; 1.3.1. ARNI predictive power
1.3.1.1. Prediction under biological and experimental noise1.3.1.2. Prediction under incomplete data; 1.3.2. ARNI expressive power; 1.3.2.1. Network motif representations; 1.3.2.2. Representing complex interactions; 1.4. ARNI assisted scientific methodology; 1.4.1. Testing biological hypotheses; 1.4.1.1. Testing cross-talk between signaling pathways; 1.4.2. Informative experiments for networks discrimination; 1.5. Related work and comparison with non-symbolic approaches; 1.5.1. Limitations and future work; 1.6. Conclusions; 1.7. Bibliography Chapter 2. Reasoning on the Response of Logical Signaling Networks with ASP2.1. Introduction; 2.2. Answer set programming at a glance; 2.3. Learn and control logical networks with ASP; 2.3.1. Preliminaries; 2.3.2. Reasoning on the response of logical networks; 2.3.3. Learning models of immediate-early response; 2.3.4. Minimal intervention strategies; 2.3.5. Software toolbox: caspo; 2.4. Conclusion; 2.5. Acknowledgments; 2.6. Bibliography; Chapter 3. A Logical Model for Molecular Interaction Maps; 3.1. Introduction; 3.2. Biological background; 3.3. Logical model 3.3.1. Activation and inhibition3.3.1.1. Activation and inhibition capacities; 3.3.1.2. Relations between the activation and inhibition causes and effects; 3.3.1.3. Relations between causal relations; 3.3.2. Model extension; 3.3.2.1. Phosphorylation; 3.3.2.2. Autophosphorylation; 3.3.2.3. Binding; 3.3.3. Causality relations redefinition; 3.3.3.1. Activation axioms; 3.3.3.2. Phosphorylation axioms; 3.3.3.3. Autophosphorylation axioms; 3.3.3.4. Binding axioms; 3.3.3.5. Inhibition axioms; 3.4. Quantifier elimination for restricted formulas; 3.4.1. Domain formulas; 3.4.2. Restricted formulas 3.4.3. Completion formulas3.4.4. Domain of domain formulas; 3.4.5. Quantifier elimination procedure; 3.5. Reasoning about interactions in metabolic interaction maps; 3.6. Conclusion and future work; 3.7. Acknowledgments; 3.8. Bibliography; Chapter 4. Analyzing Large Network Dynamics with Process Hitting; 4.1. Introduction/state of the art; 4.1.1. The modeling challenge; 4.1.2. Historical context: Boolean and discrete models; 4.1.3. Analysis issues; 4.1.4. The process hitting framework; 4.1.5. Outline; 4.2. Discrete modeling with the process hitting; 4.2.1. Motivation 4.2.2. The process hitting framework |
| Record Nr. | UNINA-9910814187903321 |
| London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||