An Architecture for Adaptive Algorithmic Hybrids

Nicholas Cassimatis, Magda Bugajska, Scott Dugas, Arthi Murugesan, Paul Bello

We describe a cognitive architecture for creating more robust intelligent systems by executing hybrids of algorithms based on different computational formalisms. The architecture is motivated by the belief that (1) most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and (2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are very difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme can integrate reactive robotic controllers, logical and probabilistic inference algorithms, frame-based formalisms and sensor-processing algorithms into one system. Existing applications involve human-robot interaction, heterogeneous information retrieval and natural language understanding. Systems built using Polyscheme demonstrate that algorithmic hybrids implemented using a focus of attention can (1) exhibit more characteristics of intelligence than individual computational methods alone and (2) deal with problems that have formerly been beyond the reach of synthetic computational intelligence.

Subjects: 2. Architectures; 3. Automated Reasoning

Submitted: Apr 24, 2007


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