- Full Description
The papers in this volume are the refereed technical papers presented at AI-2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2004. The papers in this volume present new and innovative developments in the field, divided into sections on AI Techniques I and II, CBR and Recommender Systems, Ontologies, Intelligent Agents and Scheduling Systems, Knowledge Discovery in Data and Spatial Reasoning and Image Recognition. This is the twenty-first volume in the Research and Development series. The series is essential reading for those who wish to keep up to date with developments in this important field. The Application Stream papers are published as a companion volume under the title Applications and Innovations in Intelligent Systems XII.
- Table of Contents
Table of Contents
- Extracting Finite Structure from Infinite.
- Modelling Shared Extended Mind and Collective Representational Content.
- Overfitting in Wrapper
- Based Feature Subset Selection: The Harder You Try the Worse it Gets.
- Managing Ontology Versions with a Distributed Blackboard Architecture.
- OntoSearch: An Ontology Search Engine.
- Case Based Adaptation Using Interpolation over Nominal Values.
- Automating the Discovery of Recommendation Rules.
- Incremental Critiquing.
- A Treebank
- Based Case Role Annotation Using An Attributed String Matching.
- A Combinatorial Approach to Conceptual Graph Projection Checking.
- Implementing Policy Management Through BDI.
- Exploiting Causal Independence in Large Bayesian Networks.
- A Bargaining Agent Aims to `Play Fair'.
- Resource Allocation in Communication Networks Using Market
- Based Agents.
- Are Ordinal Representations Effective?
- A Framework for Planning with Hybrid Models.
- Towards Symbolic Data Mining in Numerical Time Series.
- Support Vector Machines of Interval
- based Features for Time Series Classification.
- Neighbourhood Exploitation in Hypertext Categorization.
- Using Background Knowledge to Construct Bayesian Classifiers for Data
- Poor Domains.
- Interactive Selection of Visual Features through Reinforcement Learning.
- Imprecise Qualitative Spatial Reasoning.
- Reasoning with Geometric Information in Digital Space.
- On Disjunctive Representations of Distributions and Randomization.
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