Download A PhD RESEARCH TOPIC PROPOSAL PRESENTED BY NWEKE

A PhD RESEARCH TOPIC PROPOSAL PRESENTED BY
NWEKE CHUKWUBUIKEM DAVID
Topic: ARTIFICIAL INTELLIGENCE
Brief background of the Study:
In the area of intelligent systems, “ATTENTION” refers to the management of mental resources.
Attention exists because all moderately complex environments – and the real-world
environments of everyday life in particular – are a source of vastly greater information than can
be processed in real-time by available cognitive resources of any known intelligence, human or
otherwise. General-purpose artificial intelligence (AI) systems operating with limited resources
under time-constraints in such environments must select carefully which information will be
processed and which will be ignored. Even in the (rare) cases where sufficient resources may be
available, attention could help make better use of them. All real-world tasks come with time
limits, and managing these is a key part of the role of intelligence. Many AI researchers ignore
this fact. As a result, the majority of existing AI architectures is incorrectly based on an (explicit
or implicit) assumption of infinite or sufficient computational resources. Attention has not yet
been recognized as a key cognitive process of AI systems and in particular not of artificial
general intelligence systems. This dissertation argues for the absolute necessity of an attention
mechanism for artificial general intelligence (AGI) architectures. We examine several issues
related to attention and resource management, review prior work on these topics in cognitive
psychology and AI, and present a design for a general attention mechanism for AGI systems.
The proposed design – inspired by constructivist AI methodologies – aims at architectural and
modal independence, and comprehensively addresses and integrates all principal factors
associated with attention to date.
Aims and Objectives/Purpose

Routing emails and cell phone calls.

Automated image-based medical diagnosis

Guidance for cruise missiles and weapon systems

Automatically landing airplanes

Financial pattern recognition

Detection of credit card fraud
Methodology.
The System shall be built and fully implemented using the following Architectures:

The Ymir cognitive architecture was created with the goal of endowing artificial agents
with human-like interaction capabilities in the form of embodied multimodal dialog skills
that are task oriented and function in real-time (Thórisson 1996, 1999).

ICARUS is a cognitive architecture for embodied agents that has shown promising results
on a number of classic AI toy problems in terms of generality (Langley 2005, Langley
2006).

CHREST (Chunky Hierarchy and and REtrieval STructures) is a cognitive architecture
with a psychological focus that has been used to simulate human cognition in specific
domains, such as expert behavior and verbal learning (Gobet 2005).

The Non-Axiomatic Reasoning System (NARS) is a general-purpose intelligent
reasoning system designed for operation in real-time under conditions of insufficient
knowledge and resources (Wang, 1995).

The Non-Axiomatic Reasoning System (NARS) is a general-purpose intelligent
reasoning system designed for operation in real-time under conditions of insufficient
knowledge and resources (Wang, 1995).

AKIRA is a fully implemented open-source framework whose architecture is inspired by
biological systems and is designed for parallel, asynchronous and distributed computation
(Pezzulo 2007).

OSCAR is an implemented architecture for generally intelligent agents operating under
uncertainty and incomplete knowledge (Pollock, 2008).

OpenCog Prime (OCP) is a cognitive architecture (Goertzel 2008, Goertzel 2009) that is
closely related to the OpenCog framework (Hart 2008), which is an open source software
development framework intended for implementation of AI systems providing common
knowledge representation schemes, process scheduling and I/O systems across different
AI technologies running simultaneously.

The CLARION architecture is motivated by cognitive psychology and social simulation
(Sun 2001, 2003, 2006). It is based on dual representations using both symbolic and subsymbolic data, as well as the interaction between the two.

SOAR is one of the most mature cognitive architecture currently in development, and has
been used by many researchers worldwide during its roughly 30-year life span (Laird,
2008).
Expected Outcomes
At the end of the research project, a System will be developed to act in positions and places as
expert and solution provider in-place of human expert.
References:
1. Gobet, F., & Lane, P. C. (2005). The CHREST architecture of cognition: Listening to
empirical data. Visions of mind: Architectures for cognition and affect, 204-224.
2. Goertzel, B. (2008). OpenCog Prime: Design for a Thinking Machine. Online wikibook
at http://opencog.org.
3. Goertzel, B. (2009). OpenCogPrime: A cognitive synergy based architecture for artificial
general intelligence. In Cognitive Informatics, 2009. ICCI'09. 8th IEEE International
Conference on (pp. 60-68). IEEE.
4. Laird, J. E. (2008). Extending the Soar Cognitive Architecture. In Proceedings of the
First Conference on Artificial General Intelligence, p. 224-235. Memphis, Tenn.:
Springer
5. Langley, P. (2005). An adaptive architecture for physical agents. In Web Intelligence,
2005. Proceedings. The 2005 IEEE/WIC/ACM International Conference on (pp. 18-25).
IEEE.
6. Langley, P., & Choi, D. (2006). A unified cognitive architecture for physical agents. In
Proceedings of the National Conference on Artificial Intelligence (Vol. 21, No. 2, p.
1469). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.
7. Pezzulo, G., & Calvi, G. (2007). Designing modular architectures in the framework
AKIRA. Multiagent and Grid Systems, 3(1), 65-86. 194
8. Pollock, J. L. (2008). OSCAR: An Architecture for Generally Intelligent Agents.
Frontiers in Artificial Intelligence and Applications, p. 275-286.
9. Sun, R., Merrill, E., Peterson T. (2001). From implicit skills to explicit knowledge: A
bottom-up model of skill learning. Cognitive Science. 25: 203-244.
10. Sun, R. (2003). A Detailed Specification of CLARION 5.0. Technical report available at:
http://www.cogsci.rpi.edu/~rsun/sun.tutorial.pdf Helgi Páll Helgason.
11. Sun, R. (2006). The CLARION cognitive architecture: Extending cognitive modeling to
social simulation. In: Ron Sun (ed.), Cognition and Multi-Agent Interaction. Cambridge
University Press, New York.
12. Thórisson, K. R. (1996). Communicative humanoids: a computational model of
psychosocial dialogue skills. Doctoral dissertation, Massachusetts Institute of
Technology.
13. Thórisson, K. R. (1999). Mind model for multimodal communicative creatures and
humanoids. Applied Artificial Intelligence, 13(4-5), 449-486.
14. Wang, P. (1995). Non-Axiomatic Reasoning System: Exploring the Essence of
Intelligence. Ph.D. dissertation, Indiana University, Indiana.