Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Instance-Based Learning Algorithms
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
The emergence of linguistic structure: an overview of the iterated learning model
Simulating the evolution of language
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guest Editors' Introduction: Semisentient Robots-- Routes to Integrated Intelligence
IEEE Intelligent Systems
Language Games for Autonomous Robots
IEEE Intelligent Systems
Improving Case Representation and Case Base Maintenance in Recommender Agents
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Learning words from sights and sounds: a computational model
Learning words from sights and sounds: a computational model
Long-term learning in soar and its application to the utility problem
Long-term learning in soar and its application to the utility problem
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Connecting language to the world
Artificial Intelligence - Special volume on connecting language to the world
Metacognition in computation: a selected research review
Artificial Intelligence
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
Incremental learning of perceptual categories for open-domain sketch recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Human-Oriented Interaction With an Anthropomorphic Robot
IEEE Transactions on Robotics
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Cognitive Systems Research
Learning Visual Object Categories with Global Descriptors and Local Features
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Embodied Language Acquisition: A Proof of Concept
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Semantic Image Search and Subset Selection for Classifier Training in Object Recognition
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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Motivated by the need to support language-based communication between robots and their human users, as well as grounded symbolic reasoning, this paper presents a learning architecture that can be used by robotic agents for long-term and open-ended category acquisition. To be more adaptive and to improve learning performance as well as memory usage, this learning architecture includes a metacognitive processing component. Multiple object representations and multiple classifiers and classifier combinations are used. At the object level, the main similarity measure is based on a multi-resolution matching algorithm. Categories are represented as sets of known instances. In this instance-based approach, storing and forgetting rules optimise memory usage. Classifier combinations are based on majority voting and the Dempster-Shafer evidence theory. All learning computations are carried out during the normal execution of the agent, which allows continuous monitoring of the performance of the different classifiers. The measured classification successes of the individual classifiers support an attentional selection mechanism, through which classifier combinations are dynamically reconfigured and a specific classifier is chosen to predict the category of a new unseen object. A simple physical agent, incorporating these learning capabilities, is used to test the approach. A long-term experiment was carried out having in mind the open-ended nature of category learning. With the help of a human mediator, the agent incrementally learned 68 categories of real-world objects visually perceivable through an inexpensive camera. Various aspects of the approach are evaluated through systematic experiments.