Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
How to clear a block: A theory of plans
Journal of Automated Reasoning
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Inductive functional programming using incremental program transformation
Artificial Intelligence
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
A Methodology for LISP Program Construction from Examples
Journal of the ACM (JACM)
Natural Language Processing for PROLOG Programmers
Natural Language Processing for PROLOG Programmers
The Architecture of Cognition
Automatic Program Construction Techniques
Automatic Program Construction Techniques
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Tracing Problem Solving in Real Time: fMRI Analysis of the Subject-paced Tower of Hanoi
Journal of Cognitive Neuroscience
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
The Journal of Machine Learning Research
The importance of cognitive architectures: an analysis based on CLARION
Journal of Experimental & Theoretical Artificial Intelligence
Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Analytical Inductive Functional Programming
Logic-Based Program Synthesis and Transformation
Perspectives of Neural-Symbolic Integration
Perspectives of Neural-Symbolic Integration
Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition
Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition
An introduction to inductive programming
Artificial Intelligence Review
Towards a general framework for composing disjunctive and iterative macro-operators
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Application of theorem proving to problem solving
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
Selectively generalizing plans for problem-solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
I/O guided detection of list catamorphisms: towards problem specific use of program templates in IP
Proceedings of the 2010 ACM SIGPLAN workshop on Partial evaluation and program manipulation
Semi-analytic natural number series induction
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Hi-index | 0.00 |
We present an application of the analytical inductive programming system Igor to learning sets of recursive rules from positive experience. We propose that this approach can be used within cognitive architectures to model regularity detection and generalization learning. Induced recursive rule sets represent the knowledge which can produce systematic and productive behavior in complex situations - that is, control knowledge for chaining actions in different, but structural similar situations. We argue, that an analytical approach which is governed by regularity detection in example experience is more plausible than generate-and-test approaches. After introducing analytical inductive programming with Igor we will give a variety of example applications from different problem solving domains. Furthermore, we demonstrate that the same generalization mechanism can be applied to rule acquisition for reasoning and natural language processing.