<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.</p></abstract>

One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.

Cognitive Architectures and Autonomy: A Comparative Review

Center for Analysis & Design of Intelligent Agents, School of Computer Science Venus, 2nd fl. Reykjavik University Menntavegur 1, 101 Reykjavik, Iceland

Icelandic Institute for Intelligent Machines, 2. h. Uranus Menntavegur 1, 101 Reykjavik, Iceland

Journal of Artificial General Intelligence

{"article-title":"Cognitive Architectures and Autonomy: A Comparative Review"}

One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level...

Cognitive Architectures and Autonomy: A Comparative Review

Pubblicato online: 21 mag 2012

Pagine: 1 - 30

DOI: https://doi.org/10.2478/v10229-011-0015-3

Parole chiave
cognitive architectures, autonomy, constructivist AI, realtime, meta-learning

This content is open access.

Cognitive Architectures and Autonomy: A Comparative Review

Pubblicato online: 21 mag 2012

Pagine: 1 - 30

DOI: https://doi.org/10.2478/v10229-011-0015-3

Parole chiavecognitive architectures, autonomy, constructivist AI, realtime, meta-learning

This content is open access.

Parole chiave
cognitive architectures, autonomy, constructivist AI, realtime, meta-learning