Autore	Rocha Ana Paula
Edizione	[1st ed. 2024.]
Pubbl/distr/stampa	Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Descrizione fisica	1 online resource (507 pages)
Disciplina	006.3
Altri autori (Persone)	SteelsLuc van den HerikJaap
Collana	Lecture Notes in Artificial Intelligence
Soggetto topico	Artificial intelligence Computer science Computers Computers, Special purpose Computer networks Image processing - Digital techniques Computer vision Artificial Intelligence Theory of Computation Computing Milieux Special Purpose and Application-Based Systems Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics
ISBN	3-031-55326-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Agents -- Bees, Bats and Glowworms: Swarm Algorithms for Optimizing Industrial Plants from the Bottom-Up -- Estimating the Spread of COVID-19 Due to Transportation Networks Using Agent-Based Modeling -- Dealing with the Unpredictability of Physical Resources in Real-World Multi-Agent Systems -- Coalition Alternating-Time Temporal Logic: A Logic to Find Good Coalitions to Achieve Strategic Objectives -- Holonic Energy Management Systems: Towards Flexible and Resilient Smart Grids -- Artificial Intelligence -- Predictive Explanations for and by Reinforcement Learning -- Effects of Feature Types on Donor Journey -- Data-Efficient Offline Reinforcement Learning with Approximate Symmetries -- Dynamic Communities: A Novel Recommendation Approach for Individuals and Groups -- Towards Improving Multivariate Time-Series Forecasting Using Weighted Linear Stacking -- Study on LSTM and ConvLSTM Memory-Based Deep Reinforcement Learning -- Spatial Representation and Reasoning About Fold Strata: A Qualitative Approach -- Ranking-Based Partner Selection Strategy in Open, Dynamic and Sociable Environments -- Weight Re-Mapping for Variational Quantum Algorithms -- Effective Adaptive Strategy Selection Using Extended Fine-Tuning and CNN-Based Surrogate Model in Repeated-Encounter Bilateral Automated Negotiation -- A Novel Bagged Ensemble Approach for Accurate Histopathological Breast Cancer Classification Using Transfer Learning and Convolutional Neural Networks -- UTP: A Unified Term Presentation Tool For Clinical Textual Data Using Pattern-Matching Rules and Dictionary-Based Ontologies -- Fault Tolerant Robust Adaptive Workload Orchestration in Pure Edge Computing -- Exploring Narrative Economics: An Agent-Based Co-Evolutionary Model Featuring Nonlinear Continuous-Time Opinion Dynamics -- GaSUME: A BERT-Covered Genetic Algorithm for Text Summarization -- Segmented Glioma Classification Using Radiomics-Based Machine Learning: A Comparative Analysis of Feature Selection Techniques -- RDC-Repair: Towards a Relevance-Driven Approach for Data and Constraints Repair -- Developing Image-Based Classification Techniques to Analyse Customer Behaviour.
Record Nr.	UNISA-996589546003316

Edizione	[1st ed. 2025.]
Pubbl/distr/stampa	Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Descrizione fisica	1 online resource (XIX, 344 p. 103 illus., 84 illus. in color.)
Disciplina	004
Collana	Lecture Notes in Artificial Intelligence
Soggetto topico	Computers Image processing - Digital techniques Computer vision Computer science Computer engineering Computer networks Computers, Special purpose Artificial intelligence Computing Milieux Computer Imaging, Vision, Pattern Recognition and Graphics Theory of Computation Computer Engineering and Networks Special Purpose and Application-Based Systems Artificial Intelligence
ISBN	3-031-87330-0
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	-- Artificial Intelligence. -- Deep Neural Networks for Early Human Embryo Stages Detection in Time Lapse Sequences. -- Prompt. Exploit. Repeat: Automating Network Security Testing with LLMs. -- Combining Model Ensembles for Multi-Step Forecasts Using the Example of Flood Levels. -- What comes after transformers? – A selective survey connecting ideas in deep learning. -- Multi-Stock Movements Classification: A Decoupled Graph Diffusion Neural Network Approach. -- Detecting Parkinson’s Disease Using the Spiral and Wave Drawing Test by Means of Explainable Deep Learning. -- Deep and Shallow Machine Learning for Predicting Major Donors. -- Balancing the Trinity of Feature Selection. -- On the Feasibility of Learning Bipolar Gradual Argumentation Semantics Using Neural Architectures. -- Integrity in the AI Era: Collaborative Solutions for Programming Education. -- Greedy and Local Search-Based Constraint Optimization of the Travelling Salesman Problem. -- A Model to Manage Security Risks in IoT Systems. -- Natural Language-Based Reinforcement Learning for Acquiring Commonsense Knowledge from Partial Scenarios. -- I-AM-Birds (ImB-2): Keep Detecting Amazonian Bird Species. -- Exploring the Role of Large Language Models in Professiographic Profile Mapping through the Evaluation of Argumentative Writing in Military School Education. -- A Novel RL Framework for Early Configuration Space Exploration. -- Using Chatbot Technologies to Understand Natural Language Arguments.
Record Nr.	UNINA-9910999685003321

Pubbl/distr/stampa	Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica	1 online resource (353 pages)
Disciplina	006.3
Collana	Lecture notes in computer science. Lecture notes in artificial intelligence
Soggetto topico	Artificial intelligence Intelligent agents (Computer software)
ISBN	3-031-10161-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	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 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. 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. 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 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. 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. 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 & -- 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 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?. 4.5 What Is the Time and Space Complexity of Scalable Algorithm?.
Record Nr.	UNINA-9910585770703321

Edizione	[1st ed. 2022.]
Pubbl/distr/stampa	Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica	1 online resource (236 pages)
Disciplina	006.3
Collana	Lecture Notes in Artificial Intelligence
Soggetto topico	Artificial intelligence Artificial Intelligence
ISBN	3-031-22953-3
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Agents -- Efficient Visual Sign Assignment for Crowd Evacuation Guidance Considering Risks and Multiple Objectives -- Negotiation Protocol with Learned Handover of Important Tasks for Planned Suspensions in Multi-Agent Patrol Problems -- Artificial Intelligence -- Comparing Beta-VAE to WGAN-GP for Time Series Augmentation to Improve Classification Performance -- Safe Policy Improvement Approaches and Their Limitations -- Integrative System of Deep Classifiers Certification: Case of Convolutional Attacks -- Co-operative Multi-agent Twin Delayed DDPG for Robust Phase Duration Optimization of Large Road Networks -- Generating the Gopher's Grounds: Form, Function, Order, and Alignment -- Domain Dependent Parameter Setting in SAT Solver using Machine Learning Techniques -- More Sustainable Text Classification via Uncertainty Sampling and a Human-in-the-Loop.
Record Nr.	UNISA-996508671203316