<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the story <italic>Alice in Wonderland</italic>, Alice fell down a rabbit hole and suddenly found herself in a strange world called Wonderland. Alice gradually developed knowledge about Wonderland by observing, learning, and reasoning. In this paper we present the system A<sc>lice</sc> I<sc>n</sc> W<sc>onderland</sc> that operates analogously. As a theoretical basis of the system, we define several basic concepts of logic in a generalized setting, including the notions of domain, proof, consistency, soundness, completeness, decidability, and compositionality. We also prove some basic theorems about those generalized notions. Then we model Wonderland as an arbitrary symbolic domain and Alice as a cognitive architecture that learns autonomously by observing random streams of facts from Wonderland. Alice is able to reason by means of computations that use bounded cognitive resources. Moreover, Alice develops her belief set by continuously forming, testing, and revising hypotheses. The system can learn a wide class of symbolic domains and challenge average human problem solvers in such domains as propositional logic and elementary arithmetic.</p></abstract>

In the story Alice in Wonderland, Alice fell down a rabbit hole and suddenly found herself in a strange world called Wonderland. Alice gradually developed knowledge about Wonderland by observing, learning, and reasoning. In this paper we present the system Alice In Wonderland that operates analogously. As a theoretical basis of the system, we define several basic concepts of logic in a generalized setting, including the notions of domain, proof, consistency, soundness, completeness, decidability, and compositionality. We also prove some basic theorems about those generalized notions. Then we model Wonderland as an arbitrary symbolic domain and Alice as a cognitive architecture that learns autonomously by observing random streams of facts from Wonderland. Alice is able to reason by means of computations that use bounded cognitive resources. Moreover, Alice develops her belief set by continuously forming, testing, and revising hypotheses. The system can learn a wide class of symbolic domains and challenge average human problem solvers in such domains as propositional logic and elementary arithmetic.

Learning and Reasoning in Unknown Domains

Department of Applied Information Technology, University of Gothenburg,

Department of Mathematical Sciences, Chalmers University of Technology,

Journal of Artificial General Intelligence

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Learning and Reasoning in Unknown Domains"}

In the story Alice in Wonderland, Alice fell down a rabbit hole and suddenly found herself in a strange world called Wonderland. Alice gradually developed...

Learning and Reasoning in Unknown Domains

Publié en ligne: 23 janv. 2017

Pages: 104 - 127

DOI: https://doi.org/10.1515/jagi-2016-0002

Mots clés
autonomous agent, bounded rationality, arbitrary symbolic domain

© 2016 Claes Strannegård et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Learning and Reasoning in Unknown Domains

Publié en ligne: 23 janv. 2017

Pages: 104 - 127

DOI: https://doi.org/10.1515/jagi-2016-0002

Mots clésautonomous agent, bounded rationality, arbitrary symbolic domain

© 2016 Claes Strannegård et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
autonomous agent, bounded rationality, arbitrary symbolic domain