Evolutionary Intelligence and Communication in Societies of Virtually Embodied Agents

Authors:
Binh Nguyen;Andrew Skabar
Affiliations:
Department of Computer Science and Computer Engineering, La Trobe University, Victoria, Australia 3086;Department of Computer Science and Computer Engineering, La Trobe University, Victoria, Australia 3086
Venue:
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Year:
2009

Citing 9
Cited 0

The symbol grounding problem

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
What computers still can't do: a critique of artificial reason

What computers still can't do: a critique of artificial reason
The origins of syntax in visually grounded robotic agents

Artificial Intelligence - Special issue: artificial intelligence 40 years later
behavioral coordination structural congruence and entrainment in a simulation of acoustically coupled agents

Adaptive Behavior
Evolving mobile robots able to display collective behaviors

Artificial Life
Embodied cognition: a field guide

Artificial Intelligence
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)

How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
Grounded spoken language acquisition: experiments in word learning

IEEE Transactions on Multimedia
Evolution of communication and language using signals, symbols, andwords

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to overcome the knowledge bottleneck problem, AI researchers have attempted to develop systems that are capable of automated knowledge acquisition. However, learning in these systems is hindered by context (i.e., symbol-grounding) problems, which are caused by the systems lacking the unifying structure of bodies, situations and needs that typify human learning. While the fields of Embodied Artificial Intelligence and Artificial Life have come a long way towards demonstrating how artificial systems can develop knowledge of the physical and social worlds, the focus in these areas has been on low level intelligence, and it is not clear how, such systems can be extended to deal with higher-level knowledge. In this paper, we argue that we can build towards a higher level intelligence by framing the problem as one of stimulating the development of culture and language. Specifically, we identify three important limitations that face the development of culture and language in AI systems, and propose how these limitations can be overcome. We will do this through borrowing ideas from the evolutionary sciences, which have explored how interactions between embodiment and environment have shaped the development of human intelligence and knowledge.