Unmesh Kurup

An Architecture for Problem Solving with Diagrams

In problem solving a goal/subgoal is either solved by generating needed information from current information, or further decomposed into additional subgoals. In traditional problem solving, goals, knowledge, and problem states are all modeled as expressions composed of symbolic predicates, and information generation is modeled as rule application based on matching of symbols. In problem solving with diagrams on the other hand, an additional means of generating information is available, viz., by visual perception on diagrams. A subgoal is solved opportunistically by whichever way of generating information is successful. Diagrams are especially effective because certain types of information that is entailed by given information is explicitly available – as emergent objects and emergent relations – for pickup by visual perception. We add to the traditional problem solving architecture a component for representing the diagram as a configuration of diagrammatic objects of three basic types, point, curve and region; a set of perceptual routines that recognize emergent objects and evaluate a set of generic spatial relations between objects; and a set of action routines that create or modify the diagram. We discuss how domain-specific capabilities can be added on top of the generic capabilities of the diagram system. The working of the architecture is illustrated by means of an application scenario.

An Architecture for Adaptive Algorithmic Hybrids

We describe a cognitive architecture for creating more robust intelligent systems. Our approach is to enable hybrids of algorithms based on different computational formalisms to be executed. The architecture is motivated by some features of human cognitive architecture and the following beliefs: 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 difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. The key to this approach is the following two principles: 1) Algorithms based on very different computational frameworks (e.g., logical reasoning, probabilistic inference, and case-based reasoning) can be implemented using the same set of five common functions and 2) each of these common functions can be executed using multiple data structures and algorithms. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme in planning, spatial reasoning, robotics, and information retrieval illustrate that this approach to hybridizing algorithms enables qualitative and measurable quantitative advances in the abilities of intelligent systems.

Augmenting cognitive architectures to support diagrammatic imagination.

Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures--Soar and ACT-R, to name the most prominent--do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive architectures. We discuss the degree to which DRS, our earlier proposal for such an internal representation for diagrams, meets these requirements. In DRS, the diagrams are not raw images, but a composition of objects that can be individuated and thus symbolized, while, unlike traditional symbols, the referent of the symbol is an object that retains its perceptual essence, namely, its spatiality. This duality provides a way to resolve what anti-imagists thought was a contradiction in mental imagery: the compositionality of mental images that seemed to be unique to symbol systems, and their support of a perceptual experience of images and some types of perception on them. We briefly review the use of DRS to augment Soar and ACT-R with a diagrammatic representation component. We identify issues for further research.

An architectural framework for complex cognition

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.

Reports of the AAAI 2009 Fall Symposia

The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5–7, at the Westin Arlington Gateway in Arlington, Virginia. The Symposium Series was preceded on Wednesday, November 4 by a one-day AI funding seminar. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Architectures, (2) Cognitive and Metacognitive Educational Systems, (3) Complex Adaptive Systems and the Threshold Effect: Views from the Natural and Social Sciences, (4) Manifold Learning and Its Applications, (5) Multirepresentational Architectures for Human-Level Intelligence, (6) The Uses of Computational Argumentation, and (7) Virtual Healthcare Interaction.