{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":37,"n_cols":5,"rows_kept":37,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[14,2,14,3],[23,2,23,3],[23,4,23,5],[24,2,24,3],[24,4,24,5],[25,2,25,3],[25,4,25,5],[26,2,26,3],[27,2,27,3],[28,2,28,3],[29,2,29,3],[30,2,30,3],[32,2,32,3],[33,2,33,3],[34,2,34,3],[35,2,35,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996483162203316"]
[null,null,"Titolo",null,"Agents and artificial intelligence : 13th International Conference, ICAART 2021, virtual event, February 4-6, 2021, revised selected papers / / Ana Paula Rocha, Luc Steels, H. J. van den Herik (editors)"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-10161-8"]
[null,null,"Descrizione fisica",null,"1 online resource (353 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science. Lecture notes in artificial intelligence ; ; 13251"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Intelligent agents (Computer software)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Agents -- Specification Aware Multi-Agent Reinforcement Learning -- 1 Introduction -- 2 Foundations -- 2.1 Problem Formulation -- 2.2 Reinforcement Learning -- 2.3 Multi-Agent Reinforcement Learning -- 2.4 Reward Shaping in RL -- 3 Related Work -- 4 Smart Factory Domain -- 5 Specification Transfer -- 6 Evaluation -- 6.1 Experimental Setup -- 6.2 Analysis of Separate Reward Components -- 6.3 Scaling Challenges and Solutions -- 6.4 Safety Constraints as Reward Components -- 6.5 Case Study: Specification Compliant Run-Time Behavior -- 7 Discussion -- 8 Conclusion and Future Work -- References -- Task Bundle Delegation for Reducing the Flowtime -- 1 Introduction -- 2 Related Work -- 3 Multi-agent Situated Task Allocation -- 4 Consumption and Reallocation -- 5 Negotiation Strategy -- 5.1 Peer Modelling -- 5.2 Acceptability Rule -- 5.3 Offer Strategy -- 5.4 Acceptation Strategy -- 5.5 Agent Behaviour -- 6 Results and Discussion -- 6.1 Context of Experiments -- 6.2 Classical Heuristic and Acceptability Criterion -- 6.3 N-Ary Delegation -- 6.4 Distributed Constraint Optimization Problem (DCOP) -- 7 Conclusion -- References -- A Detailed Analysis of a Systematic Review About Requirements Engineering Processes for Multi-agent Systems -- 1 Introduction -- 2 Background -- 2.1 Requirements Engineering -- 2.2 Belief-Desire-Intention Model -- 3 Related Works -- 4 Research Method -- 4.1 The Research Questions -- 4.2 Identifying and Selecting Primary Studies -- 4.3 Inclusion and "]
[{"$nulls":4},"Exclusion Criteria -- 4.4 Studies Quality Assessment -- 4.5 Data Extraction Strategy -- 4.6 Conducting the Review -- 4.7 Data Extraction -- 5 Results -- 5.1 Methodologies Analysed in the Systematic Review -- 5.2 Coverage in Relation to the Requirements Engineering Subareas Defined in SWEBOK -- 5.3 Methodologies Supporting the BDI Model."]
[{"$nulls":4},"5.4 Gaps Found in This Review -- 6 Threats to Validity -- 7 Insights for Future Works -- 8 Conclusions and Future Works -- References -- Automatically-Generated Agent Organizations for Flexible Workflow Enactment -- 1 Introduction -- 2 A Running Example -- 3 From Business Process Models to Agent Organizations -- 3.1 Ontology and Goals from a Business Process -- 3.2 Mapping Goals to Agents via Organization -- 3.3 The BPMN2MOISE Tool -- 4 The Automatic Definition of Organizations -- 4.1 Heuristic for the Structural Specification: Roles and Groups -- 4.2 Heuristic for the Functional Specification: Goals and Plans -- 4.3 Heuristic for the Normative Specification -- 5 Conclusions -- References -- Negotiation Considering Privacy Loss on Asymmetric Multi-objective Decentralized Constraint Optimization Problem -- 1 Introduction -- 2 Background -- 2.1 Asymmetric Constraint Optimization Problem with Cost of Private Information to Be Published -- 2.2 Criteria and Measurement of Social Welfare -- 2.3 Decentralized Complete Solution Method for Asymmetric Multi-objective Constraint Optimization Problems Based on Pseudo-trees and Dynamic Programming -- 3 Decentralized Solution Framework for Selection of Utility Values to Be Published and Solution of Published Problems -- 3.1 Basic Design of Proposed Framework -- 3.2 Selection of Newly Published Utility Values -- 3.3 Evaluation Criteria for Utility Values to Be Published -- 3.4 Hierarchically Structured Cost Vector Integrating Criteria for Publication of Utility Values -- 3.5 Solving Problems with Published Utility Values -- 4 Additional Preprocessing Methods -- 4.1 Trading Utility Between Neighborhood Agents -- 4.2 Approximation of Binary Functions -- 5 Evaluation -- 5.1 Settings of Experiment -- 5.2 Experimental Results -- 6 Discussion -- 7 Conclusion -- References -- Artificial Intelligence."]
[{"$nulls":4},"Utilizing Out-Domain Datasets to Enhance Multi-task Citation Analysis -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Classification -- 2.2 Intent Classification -- 2.3 Out-Domain Data Utilization -- 3 Datasets -- 3.1 Sentiment Datasets -- 3.2 Intent Dataset -- 4 Contributions -- 4.1 ImpactCite -- 4.2 Overcoming Data Scarcity and Data Feeding Techniques -- 4.3 Fusion Approach -- 5 Experiments and Analysis -- 5.1 Intent Classification -- 5.2 Sentiment Classification -- 5.3 Out-Domain: Evaluating Impact of Additional Data -- 5.4 Multi-task Model: Fusing Scientific Sentiment and Intent -- 6 Discussion -- 7 Conclusion -- References -- Using Possibilistic Networks to Compute Learning Course Indicators -- 1 Introduction -- 2 Possibility Theory -- 3 Message Passing Inference -- 4 Compiling Possibilistic Networks -- 5 Experimentation -- 5.1 Presentation -- 5.2 Results -- 6 Conclusion -- References -- Assured Deep Multi-Agent Reinforcement Learning for Safe Robotic Systems -- 1 Introduction -- 2 Background -- 2.1 Markov Decision Process -- 2.2 Multi-Agent Markov Decision Process -- 2.3 Abstract Markov Decision Process -- 2.4 Single-Agent Reinforcement Learning -- 2.5 Multi-Agent Reinforcement Learning -- 2.6 Deep Reinforcement Learning -- 2.7 Quantitative Verification -- 3 Domain Example -- 4 Approach -- 5 Evaluation -- 5.1 Experimental Set-Up -- 5.2 Radiation Avoidance Patrolling Domain -- 5.3 Multi-Agent Guarded Flag Collection -- 6 Related Work -- 7 Conclusion -- References -- How to Segment Handwritten Historical Chronicles Using Fully Convolutional Networks? -- 1 Introduction -- 2 Related Work -- 2.1 "]
[{"$nulls":4},"Methods -- 2.2 Datasets -- 3 FCN Architecture -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Input Resolution -- 5.2 Loss Function Weighting -- 5.3 Training Data Extension -- 5.4 Combined Setup -- 5.5 Post-processing -- 5.6 Transfer Learning."]
[{"$nulls":4},"6 Porta Fontium Integration and Method Tuning -- 7 Conclusions and Future Work -- References -- On the Relationship with Toulmin Method to Logic-Based Argumentation -- 1 Introduction -- 2 Preliminaries -- 2.1 Toulmin Model of Argumentation -- 2.2 NDSA: Natural Deduction for Structured Argumentation -- 3 The Reception and Refinement of Toulmin's Model in Logic-Based Argumentation -- 3.1 Reasoning on NDSA and Admissible Sets -- 3.2 2-Tier AF: Two-Tier Argumentation Framework -- 4 Related Work -- 5 Conclusion and Future Direction -- References -- Informer: An Efficient Transformer Architecture Using Convolutional Layers -- 1 Introduction -- 2 Previous Work -- 3 Information Organization Layer -- 4 Experiments -- 4.1 Training Details -- 4.2 Results Analysis -- 5 Conclusions -- References -- Improving the Generalization of Deep Learning Classification Models in Medical Imaging Using Transfer Learning and Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Learning in Medical Imaging -- 2.2 Generative Adversarial Networks in Medical Imaging -- 3 Materials and Methods -- 3.1 Dataset Description and Pre-processing -- 3.2 Transfer Learning Models -- 3.3 Generative Adversarial Networks (GAN) -- 4 Results -- 4.1 Comparison of Results with Other Recent Similar Works -- 5 Discussion -- 6 Conclusions and Future Work -- References -- An Interpretable Word Sense Classifier for Human Explainable Chatbot -- 1 Introduction -- 2 Related Work -- 3 Proposed Tsetlin Machine Based Word Sense Disambiguation -- 3.1 Tsetlin Machine -- 3.2 Training of the Proposed WSD Model -- 4 Performance and Interpretation of WSD -- 4.1 Results -- 4.2 Explainable WSD -- 4.3 Application of Interpretation in Chatbot -- 5 Conclusion -- References -- A Tsetlin Machine Framework for Universal Outlier and Novelty Detection -- 1 Introduction -- 2 Related Work."]
[{"$nulls":4},"3 Tsetlin Machine Framework -- 3.1 TM Architecture for Generator -- 3.2 Classifier -- 4 Experiments and Results -- 4.1 Outlier Detection -- 4.2 Novelty Detection in Text -- 5 Conclusions -- References -- Adding Supply/Demand Imbalance-Sensitivity to Simple Automated Trader-Agents -- 1 Introduction -- 2 Prior Work -- 2.1 Automated Traders -- 2.2 Critique of Church &amp -- Cliff -- 2.3 Measuring Imbalance -- 3 Adding MLOFI-Impact to Robot Traders -- 3.1 Simple Robot Traders with Impact: AA, ZIP, and ISHV -- 3.2 MLOFI Opinionated PRZI Traders for Narrative Economics -- 4 Discussion and Conclusion -- References -- Advances in Measuring Inflation Within Virtual Economies Using Deep Reinforcement Learning -- 1 Introduction -- 1.1 Proposed System -- 2 Related Works -- 2.1 Parameter Tuning -- 2.2 Game Balance -- 2.3 Automated Game Testing -- 3 History of Economies in Games -- 3.1 Eve Online -- 4 Testing Economies -- 5 Parameter Tuning -- 6 Methods -- 6.1 Economy Design -- 6.2 Adventure Agents -- 6.3 Co-operative Behaviours -- 6.4 Crafting Agents -- 6.5 Training -- 6.6 Economy Sinks -- 6.7 Training -- 6.8 Parameter Tuning -- 6.9 Data Collection -- 7 Inflation Results -- 7.1 Results - Parameter Tuning -- 8 Discussion and Future Work -- References -- Practical City Scale Stochastic Path Planning with Pre-computation -- 1 Introduction -- 2 Previous Work -- 3 Framework -- 3.1 City and Edge Weights -- 3.2 Traffic Data -- 3.3 Open Street Map -- 3.4 Agents -- 3.5 City Graph Partitioning -- 3.6 Exemplar Assignment -- 3.7 Base Path Planning Framework -- 3.8 Pre-processing: Building Distance Oracles -- 3.9 Scalable Algorithm -- 4 Experiments and Results -- 4.1 How Many Partitions Are Needed to "]
[{"$nulls":4},"Represent the City Graph? -- 4.2 Which Partitioning Method We Picked? -- 4.3 Which Exemplar Assignment Approach Is the Best? -- 4.4 How Is the Quality of Approximate Paths?."]
[{"$nulls":4},"4.5 What Is the Time and Space Complexity of Scalable Algorithm?."]
[null,"2.","Record Nr.",null,"UNISANNIOMIL0241916"]
[null,null,"Autore",null,"Bobbio, Scipione"]
[null,null,"Titolo",null,"Esercizi di elettrotecnica / Scipione Bobbio"]
[null,null,"Pubbl/distr/stampa",null,"Napoli, : CUEN, \\1995!"]
[null,null,"ISBN",null,"8871462688"]
[null,null,"Edizione",null,"[2. ed. riv.]"]
[null,null,"Descrizione fisica",null,"VI, 618 p. : ill. ; 24 cm."]
[null,null,"Disciplina",null,"621.3"]
[{"$nulls":4},"621.3076"]
[null,null,"Soggetti",null,"Elettrotecnica - Esercizi"]
[null,null,"Collocazione",null,"SALA DING 621.3076                BOB.es01SALA DING 621.3076                BOB.es"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]