An architectural framework for complex cognition

Abstract

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.

Introduction

According to Sternberg and Ben-Zeev (2001), complex cognition “deals with how people mentally represent and think about information.” Cognition can be complex in a number of ways. In one sense, any task, can be made complex by adding sufficient rules or constraints to the original task description. In these cases, the complexity of the problem arises from the depth of detail and knowledge needed to solve the particular task, and some combination of heuristic and brute force approaches are often sufficient to find the solution. Complexity can also arise as a function of diversity, where instead of a single-task, the agent is faced with a variety of tasks over the course of problem solving. Success in such problems is not based on any single heuristic or algorithm. Instead, there are many studies that show that the choice of representation makes a significant difference (Larkin et al., 1980, Kaplan and Simon, 1990, Elia et al., 2007, Larkin and Simon, 1987, van Someren et al., 1998). Consider any of the spatial reasoning questions commonly found on standardized tests. Solving them is often as easy as creating the right diagram and reading off the answer. Attempting to solve the same question with a logic approach is more complicated and takes more time. This choice of which representation to use is made during the design process by the agent designer, and is very rarely part of the problem solving process of the agent. While this approach is suitable for problems that have fixed tasks, when confronting a diverse set of problems, it is unlikely that the choice of a single representation will produce the correct agent behavior. Thus, dynamically choosing an appropriate representation is a powerful strategy for successfully solving complex cognitive tasks.

Representations can be distinguished at a couple of levels. They can be distinct at the level of representational structure like in the case of logic/predicate calculus representations and array-based spatial representations. In this paper, we refer to this level as the level of the representational formalism. Representations can also be different based on the kinds of operations that are performed on the structure. For example, you can imagine the same predicate calculus representational formalism having a set of logic inference rules for backpropagation or implementing a Bayesian framework. These distinctions are not exclusive though a particular structure often lends itself to a particular kind of processing. Choosing the right representation involves making choices in both these dimensions. However, only the first choice, that of the formalism, is part of the architecture of the agent; the agent architecture must be designed to support the existence of a number of representations that differ at the processing levels by providing the necessary scaffolding (the structural variety). It is this aspect of the representational process that we tackle with the use of the Polyscheme architecture, namely, the existence of different structural formalisms that are then available to the agent to represent and reason about a variety of problem.

The use of appropriate representations also raises a second issue of how results from multiple representations are integrated over the course of the problem solving process. For example, if you have a symbolic representation of the physics of the world (a naive physics model) and a spatial/diagrammatic representation of the layout of the same world, how does the architecture support the integration of reasoning that is done over these multiple representations? Again, there are at least two possibilities. The first way is for the agent to choose the appropriate formalism for each sub-problem (the spatial representation is used to reason about the trajectory of a thrown brick and whether its path intersects a mirror in the world and the symbolic representation for reasoning about the effects of the brick hitting the mirror). Each step of the problem solving process is done by the appropriate representation and integration is simply the modification of a common working memory. The problem with this approach is the original problem of choosing the appropriate representation. The second way, proposed in this paper, is to maintain multiple representations during every step of the problem solving process, instead of selecting any single representation for each step. Integration is more difficult in this case but the agent, as a whole, is more flexible to the demands of the environment and the task. The Polyscheme cognitive architecture provides both – multiple formalisms for representation, and an architectural framework that integrates the results of reasoning at every step to drive the problem solving process forward.

We evaluate the Polyscheme architecture’s suitability for solving a variety of tasks where representational formalism is an important factor in finding successful solutions. In particular, we describe Polyscheme agents problem solving in three tasks from the domains of constraint satisfaction, language understanding and planning problems. These domains were chosen to provide wide coverage over a class of tasks important for both human and artificial cognition. While the individual tasks we describe are fairly straightforward and could be solved using traditional symbolic approaches, our evaluation shows how flexibility in representational formalism can be leveraged by the architecture for these three widely different but important problem domains, and show significant effects even in small-sized problems. The coordination of multiple representations allows for a reduction in inferential complexity, improvement in scaling, and compactness of representation. Further research is directed to show how these benefits extend upward to larger cognitive tasks