12512nam 22007455 450 99656586540331620231126161933.0981-9983-91-610.1007/978-981-99-8391-9(MiAaPQ)EBC30974070(Au-PeEL)EBL30974070(DE-He213)978-981-99-8391-9(EXLCZ)992908401010004120231126d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAI 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 Wang1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (509 pages)Lecture Notes in Artificial Intelligence,2945-9141 ;14472Print version: Liu, Tongliang AI 2023: Advances in Artificial Intelligence Singapore : Springer,c2024 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 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 -- 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 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.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.Lecture Notes in Artificial Intelligence,2945-9141 ;14472Artificial intelligenceComputer networksData miningApplication softwareComputer visionArtificial IntelligenceComputer Communication NetworksData Mining and Knowledge DiscoveryComputer and Information Systems ApplicationsComputer VisionArtificial 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.006.3Liu Tongliang1448665Webb Geoff1448666Yue Lin1448667Wang Dadong1448668MiAaPQMiAaPQMiAaPQBOOK996565865403316AI 2023: Advances in Artificial Intelligence3644361UNISA