A Hybrid Symbolic and Sub-Symbolic Intelligent System for Mobile Robots

Journal of Aerospace Computing, Information, and Communication

2010

This paper discusses the psychological, philosophical, and neurological definitions of consciousness and the prospects for the development of conscious machines or robots in the foreseeable future. Various definitions of consciousness are introduced and discussed within the different fields mentioned. A conscious machine or robot may be within the realm of engineering possibilities if current technological developments, especially Moore's law, continue at their current pace. Given the complexity of cognition and consciousness a hybrid parallel architecture with significant input/output appears to offer the best solution for the implementation of a complex system of systems which functionally approximates a human mind. Ideally, this architecture would include traditional symbolic representations as well as distributed representations which approximate the nonlinear dynamics seen in the human brain.

Section: Computer Science and Aimentioning

confidence: 99%

Review of Consciousness and the Possibility of Conscious Robots

Journal of Aerospace Computing, Information, and Communication

2010

“…SS-RICS can also access "concepts" which are long term facts; similar to declarative memories within ACT-R, but within SS-RICS they are considered long term memories (i.e., memories which do not decay). SS-RICS can also generate productions automatically in order to generalize [17]. SS-RICS has two types of processes for the symbolic generation of new productions, top-down learning and bottom-up learning.…”

Section: Hybrid Approach To Complex Cognitionmentioning

confidence: 99%

“…Examples of these are Soar [14], ACT-R [15], and EPIC [16]. Some of these have been implemented on mobile robots [4,17].…”

Section: Cognitive Architecturesmentioning

confidence: 99%

Deep Blue Cannot Play Checkers: The Need for Generalized Intelligence for Mobile Robots

2010

Journal of Robotics

Generalized intelligence is much more difficult than originally anticipated when Artificial Intelligence (AI) was first introduced in the early 1960s. Deep Blue, the chess playing supercomputer, was developed to defeat the top rated human chess player and successfully did so by defeating Gary Kasporov in 1997. However, Deep Blue only played chess; it did not play checkers, or any other games. Other examples of AI programs which learned and played games were successful at specific tasks, but generalizing the learned behavior to other domains was not attempted. So the question remains: Why is generalized intelligence so difficult? If complex tasks require a significant amount of development, time and task generalization is not easily accomplished, then a significant amount of effort is going to be required to develop an intelligent system. This approach will require a system of systems approach that uses many AI techniques: neural networks, fuzzy logic, and cognitive architectures.

“…The architecture of SS-RICS [48,51] shown in Figure 5 is one attempt at a general-purpose intelligent system. This system uses a cognitive architecture (based on ACT/R) for decision making but allows for sub-symbolic processing, such as neural networks, for processing sensor data.…”

Section: An Engineering Approach To Developing Conscious Systemsmentioning

confidence: 99%

The Requirements and Possibilities of Creating Conscious Systems

AIAA Infotech@Aerospace Conference

2009

Self Cite

This paper discusses the psychological, philosophical and neurological definitions of consciousness and the prospects for the development of a conscious machine in the foreseeable future. Various definitions of consciousness are introduced and discussed within the different fields mentioned. We conclude that a conscious machine may be within the realm of engineering possibilities if current technological developments, especially Moore's Law, continue at their current pace. Given the complexity of cognition and consciousness a hybrid architecture appears to offer the best solution for the implementation of a complex system of systems which functionally approximates a human mind. Ideally, this architecture would include traditional symbolic representations as well as distributed representations which approximate the nonlinear dynamics seen in the human brain.