A self-organizing spatial vocabulary
Artificial Life
The Origins of Ontologies and Communication Conventions in Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
Language Games for Autonomous Robots
IEEE Intelligent Systems
Situated Grounded Word Semantics
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Cross-situational learning: a mathematical approach
EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
Integrating high-level cognitive systems with sensorimotor control
Advanced Engineering Informatics
Combining different interaction strategies reduces uncertainty when bootstrapping a lexicon
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
Lingodroids: socially grounding place names in privately grounded cognitive maps
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Simulating the emergence of grammatical agreement in multi-agent language games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Beyond here-and-now: extending shared physical experiences to shared conceptual experiences
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Pattern Recognition Letters
A cognitive approach for robots' autonomous learning
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Robotics and Autonomous Systems
Hi-index | 0.00 |
Learning the meanings of words requires coping with referential uncertainty-a learner hearing a novel word cannot be sure which aspects or properties of the referred object or event comprise the meaning of the word. Data from developmental psychology suggest that human learners grasp the important aspects of many novel words after just a few exposures, a phenomenon known as fast mapping. Traditionally, word learning is viewed as a mapping task, in which the learner has to map a set of forms onto a set of pre-existing concepts. We criticise this approach and argue instead for a flexible nature of the coupling between form and meanings as a solution to the problem of referential uncertainty. We implemented and tested the model in populations of humanoid robots that play situated language games about objects in their shared environment. Results show that the model can handle an exponential increase in uncertainty and allows scaling towards very large meaning spaces, while retaining the ability to grasp an operational meaning almost instantly for a great number of words. In addition, the model captures some aspects of the flexibility of form-meaning associations found in human languages. Meanings of words can shift between being very specific (names) and general (e.g. 'small'). We show that this specificity is biased not by the model itself but by the distribution of object properties in the world.