SOAR: an architecture for general intelligence
Artificial Intelligence
Intelligence without representation
Artificial Intelligence
An algorithm for pronominal anaphora resolution
Computational Linguistics
Multimodal Representations as Basis for Cognitive Architecture
Proceedings of the IFIP 17th World Computer Congress - TC12 Stream on Intelligent Information Processing
A New, Fully Automatic Version of Mitkov's Knowledge-Poor Pronoun Resolution Method
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
An architecture for anaphora resolution
ANLC '88 Proceedings of the second conference on Applied natural language processing
Automatic processing of large corpora for the resolution of anaphora references
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
A cognitive substrate for achieving human-level intelligence
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
ACT-R: a theory of higher level cognition and its relation to visual attention
Human-Computer Interaction
An architecture for adaptive algorithmic hybrids
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Predicate projection in a bimodal spatial reasoning system
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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Any non-trivial task requires an appropriate representational formalism. Usually, for single-task or single-domain problems this choice of formalism is not explicitly made by the agent itself, but by the agent designer, and is implicit in the choice of data structures and algorithms used by the agent. However, complex cognition involves domains where the type of problems that the agent is expected to solve is not clear at the outset. Instead, at each stage of the problem solving process, the agent is expected to choose an appropriate formalism, solve the problem and integrate these results over the course of the entire problem solving episode. In this paper, we present one approach to solving two of the above problems - how does an agent choose the right representation and how can it integrate results from multiple representations over the course of problem solving? We present this approach in the context of Polyscheme, a cognitive architecture that is strongly integrated, focused on inference and adaptive to new information. We describe the representational formalisms and associated processes present in Polyscheme (propositional and spatial) and the decision cycle that allows information from multiple representations to be integrated. Using examples from complex tasks such as constraint satisfaction, language understanding and planning, we show how a Polyscheme agent can show improved performance by leveraging its multiple representations without the hindsight of representational choice.