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Record Nr. |
UNINA9910766896603321 |
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Autore |
Liu Tongliang |
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Titolo |
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part II / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (509 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14472 |
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Altri autori (Persone) |
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WebbGeoff |
YueLin |
WangDadong |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computer networks |
Data mining |
Application software |
Computer vision |
Artificial Intelligence |
Computer Communication Networks |
Data Mining and Knowledge Discovery |
Computer and Information Systems Applications |
Computer Vision |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Knowledge Representation and NLP -- Collaborative Qualitative Environment Mapping -- 1 Introduction -- 2 Qualitative Spatio-Temporal Reasoning -- 3 LH Interval Calculus -- 4 Collaborative Qualitative Environmental Mapping -- 5 Experiments -- 6 Related Work -- 7 Conclusion and Future Work -- References -- Towards Learning Action Models from Narrative Text Through Extraction and Ordering of Structured Events -- 1 Introduction -- 2 Related Work -- 3 Structured |
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Event Extraction -- 4 Event Ordering -- 5 Narrative Chain Extraction -- 6 Challenges for NLP Research -- 7 Challenges for Model Acquisition -- 8 Conclusions -- References -- The Difficulty of Novelty Detection and Adaptation in Physical Environments -- 1 Introduction -- 2 Background and Related Work -- 2.1 Novelty Research -- 2.2 Difficulty Prediction -- 2.3 Learning Algorithms -- 2.4 Qualitative Spatial Relations (QSRs) -- 2.5 Experimental Domain -- 3 Novelty Difficulty Formulation -- 3.1 Dimensions of Novelty -- 3.2 Observational State -- 3.3 Action State -- 4 Discussion and Conclusion -- References -- Lateral AI: Simulating Diversity in Virtual Communities -- 1 Introduction -- 1.1 Large Language Models -- 1.2 Prompt Engineering -- 2 Lateral AI -- 2.1 Lateral AI Design -- 2.2 Comparison with Other Models -- 2.3 Key Features of Lateral AI -- 3 Lateral AI Demonstrations -- 3.1 Arnold Schwarzenegger AI Persona's Advice on Vitality -- 3.2 Creating Unconventional Thinkers -- 3.3 A Moral Dilemma -- 3.4 Seeking Recommendations from a Board of Experts -- 3.5 Pushing AI To Predict Beyond Its Factual Knowledge -- 4 Conclusion -- References -- Reports, Observations, and Belief Change -- 1 Introduction -- 2 Preliminaries -- 2.1 Motivating Example -- 2.2 Belief Revision -- 2.3 Trust -- 3 Revision by Reports. |
3.1 Basic Definitions -- 3.2 Report Revision Operators -- 3.3 Honesty Sets -- 3.4 Representation Result -- 4 Observations -- 4.1 Conflict -- 4.2 Revision by Observations -- 4.3 Basic Properties -- 5 Discussion -- 5.1 Related Work -- 5.2 Future Work -- 5.3 Conclusion -- References -- A Prompting Framework to Enhance Language Model Output -- 1 Introduction -- 2 Prompting Techniques -- 3 Research Methods -- 3.1 Framework Formulation -- 4 Results -- 4.1 Experiments -- 4.2 Intrinsic Evaluation Results -- 4.3 Extrinsic Evaluation Results -- 4.4 Constraints -- 5 Conclusions -- References -- Epistemic Reasoning in Computational Machine Ethics -- 1 Introduction -- 2 Background -- 3 Ethical Principle Function -- 3.1 Goodness-Based Principle -- 3.2 Less Harm Principle -- 3.3 Deontological Principle -- 4 Aggregation Strategies -- 4.1 Maximum Average -- 4.2 Maximin Strategy -- 4.3 Coefficient of Optimism -- 4.4 Regret Minimisation -- 4.5 Illustration -- 5 Results and Discussions -- 5.1 Milnor's Axioms -- 5.2 Axiom Satisfaction -- 6 Conclusion -- References -- Using Social Sensing to Validate Flood Risk Modelling in England -- 1 Introduction -- 2 Methodology -- 2.1 Data Collection -- 2.2 Twitter Data Pre-processing -- 2.3 Flood Map Development -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Symbolic Data Analysis to Improve Completeness of Model Combination Methods -- 1 Introduction -- 2 Background -- 2.1 The Symbolic Data Analysis Paradigm -- 2.2 Consensus Models -- 3 Build a Decision Tree from a Symbolic Data Table -- 3.1 Build a Decision Tree from a Synthetic Dataset -- 3.2 Build a Decision Tree from Symbolic Distributional Data -- 4 Evaluation and Results -- 4.1 Datasets -- 4.2 Results and Discussion -- 5 Conclusions -- References -- CySpider: A Neural Semantic Parsing Corpus with Baseline Models for Property Graphs -- 1 Introduction. |
2 Related Work -- 3 Notation and Task Formulation -- 4 SQL2Cypher: From SQL Queries to Cypher Queries -- 5 Text-to-Cypher Neural Models -- 5.1 Pipeline -- 5.2 End-to-End Training -- 5.3 Evaluation Metric -- 6 Experiment Results -- 6.1 Dataset Statistics -- 6.2 Models Evaluation Result -- 6.3 Error Analysis -- 7 Conclusion and Future Work -- References -- S5TR: Simple Single Stage Sequencer for Scene Text Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparisons with the State-of-the-arts -- 4.4 Qualitative Results -- 5 Conclusions -- References -- |
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Explainable AI -- Coping with Data Distribution Shifts: XAI-Based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Energy Consumption Prediction -- 2.2 XAI-Based Model Improvement -- 2.3 SHapley Additive ExPlanations (SHAP) -- 3 SHAP Clustering-Based Adaptive Learning (SCAL) -- 3.1 Building Block 1: SHAP Clustering in Explanation Space -- 3.2 Building Block 2: Extraction of SHAP Clustering Characteristics -- 3.3 Building Block 3: Adaptive Model Refinement Based on SHAP Clustering Characteristics -- 4 Experimental Setup and Data Set -- 5 Results -- 5.1 SCAL Performance -- 5.2 Cluster Analysis in Explanation Space -- 6 Transferability to Other Use Cases -- 6.1 Financial Distress Data Set (Classification Problem) -- 6.2 Power Data Set (Regression Problem) -- 7 Conclusion and Future Work -- References -- Concept-Guided Interpretable Federated Learning -- 1 Introduction -- 2 Related Work -- 2.1 Interpretable Federated Learning -- 2.2 Concept-Related Interpretability -- 3 Problem Settings -- 3.1 Federated Learning -- 3.2 Concept Bottleneck Model -- 4 Proposed Method -- 4.1 Concept Bank -- 4.2 Linear Predictor. |
4.3 Training Algorithm -- 5 Experiment -- 5.1 Datasets -- 5.2 Performance Analysis -- 5.3 Reasoning Process -- 6 Conclusion and Limitations -- References -- Systematic Analysis of the Impact of Label Noise Correction on ML Fairness -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experiments -- 5 Results -- 6 Discussion -- 7 Conclusions -- References -- Part-Aware Prototype-Aligned Interpretable Image Classification with Basic Feature Domain -- 1 Introduction -- 2 Method -- 2.1 The Overview of PaProtoPNet Architecture -- 2.2 Basic Feature Domain and Prototype Alignment -- 2.3 Feature Separation Module -- 2.4 Computing Scores for Classification -- 2.5 Overall Loss Function -- 3 Experiments -- 3.1 Performance Comparison -- 3.2 Model Analysis -- 3.3 Reasoning Process -- 4 Discussion and Future Work -- References -- Hybrid CNN-Interpreter: Interprete Local and Global Contexts for CNN-Based Models -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network Structures -- 2.2 Visual Interpretabilities of CNN-Based Models -- 3 Method -- 3.1 Stacking Forward Propagation -- 3.2 Linear Regression Module -- 3.3 Filter Importance Analysis Module -- 4 Experiment and Discussion -- 4.1 Local Interpretability for CNN-Based Models -- 4.2 Global Interpretability for CNN-Based Models -- 5 Conclusion and Future Work -- References -- Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust -- 1 Introduction -- 2 Related Work -- 2.1 User Trust -- 2.2 Fidelity -- 2.3 Robustness -- 3 Hypotheses -- 4 Methodology -- 4.1 Fidelity-Based Scenario Study Design -- 4.2 Robustness-Based Scenario Study Design -- 4.3 Metrics -- 5 Experiment -- 5.1 Dataset -- 5.2 Participants -- 5.3 Experimental Procedure -- 6 Results -- 6.1 Correlations Between User Trust and Fidelity -- 6.2 Correlations Between User Trust and Robustness -- 7 Discussion. |
8 Conclusion and Future Work -- References -- Interpretable Drawing Psychoanalysis via House-Tree-Person Test -- 1 Introduction -- 2 Related Works -- 2.1 Drawing Psychoanalysis -- 2.2 Class Activation Mapping -- 3 Method -- 3.1 Quantization of the Size -- 3.2 Quantization of the Position -- 4 Experiments -- 4.1 The HTP Dataset -- 4.2 Implementation Detials -- 4.3 Experiment Results -- 5 Conclusion -- References -- A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection -- 1 Introduction -- 1.1 Main Contributions -- 2 Collection of Polynomial SGaBloME Models -- 2.1 Variable Selection via Selecting Relevant Variables -- 2.2 Variable Selection via Rank Sparse |
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Models -- 2.3 Collection of Polynomial SGaBloME Models -- 3 Main Theoretical Results -- 3.1 Boundedness Conditions on the Parameter Space -- 3.2 Loss Function -- 3.3 Penalized Maximum Likelihood Estimation (PMLE) -- 3.4 Oracle Inequality -- 4 Conclusion and Perspectives -- References -- Reinforcement Learning -- Auction-Based Allocation of Location-Specific Tasks -- 1 Introduction -- 2 Setup -- 3 Auction-Based Algorithms -- 3.1 Bidding Rule -- 3.2 BidSumPath Bidding Rule -- 3.3 BidSumTree Bidding Rule -- 4 Theoretical Analysis Under Task Capacities -- 5 Experimental Comparison of Algorithms -- 5.1 Experimental Setup and Design -- 5.2 Impact of Feasibility Constraints -- 5.3 Performance Against Optimal -- 6 Conclusions -- References -- Generalized Bargaining Protocols -- 1 Introduction -- 2 Automated Negotiation -- 3 Proposed Framework -- 3.1 Evaluating Negotiation Protocols -- 3.2 Tentative Agreements Unique Offers (TAU) -- 4 Empirical Evaluation -- 5 Conclusion -- References -- SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 SAGE. |
4.1 Meta-controller. |
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Sommario/riassunto |
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This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm. |
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