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Towards a Complete, Multi-Level Cognitive Architecture

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Towards a Complete, Multi-Level Cognitive Architecture Towards a Complete, Multi-level Cognitive Architecture Robert Wray1 ([email protected]), Christian Lebiere2 ([email protected]), Peter Weinstein3 ([email protected]), Krishna Jha4 ([email protected]), Jonathan Springer5 ([email protected]), Ted Belding6 ([email protected]), Bradley Best7 ([email protected]) & Van Parunak6 ([email protected] ) 1Soar Technology, Inc., 3600 Green Court Suite 600, Ann Arbor, MI 48105 2Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA 15213 3Altarum Institute, 3520 Green Court Suite 300, Ann Arbor, MI 48105 4Lockheed Martin Advanced Technology Labs, 3 Executive Campus, Cherry Hill, NJ 08002 5Reservoir Labs, 632 Broadway Suite 803, New York, NY 10012 6NewVectors, 3520 Green Court Suite 250, Ann Arbor, MI 48105 7Adaptive Cognitive Systems LLC, 1709 Alpine Avenue, Boulder, CO 80304 Abstract situations; and 3) satisficing (“good enough”) reasoning. As an example, Best (2005) found that humans generate The paper describes a novel approach to cognitive reasonably good solutions to the NP-complete traveling architecture exploration in which multiple cognitive salesman problem (TSP) in nearly linear time for problems architectures are integrated in their entirety. The goal is to up to about 40 cities. Humans accomplish this via parallel increase significantly the application breadth and utility of cognitive architectures generally. The resulting architecture perceptual processes that group cities by proximity, favors a breadth-first rather than depth-first approach to evaluation of goodness of path (dependent on experience cognitive modeling by focusing on matching the broad power and memory), and a localized, serial search process. We of human cognition rather than any specific data set. It uses introduce C3I1 (“three cognitions, one intelligence.”) a human cognition as a functional blueprint for meeting the novel cognitive architecture that integrates three cognitive- requirements for general intelligence. For example, a chief architecture-level approaches, each targeted to one of these design principle is inspired by the power of human perception distinguishing human capacities. and memory to reduce the effective complexity of problem Our approach is similar to the common approach to solving. Such complexity reduction is reflected in an cognitive architecture development, which adapts and emphasis on integrating subsymbolic and statistical mechanisms with symbolic ones. The architecture realizes a incorporates existing algorithms into an architecture. “cognitive pyramid” in which the scale and complexity of a Cassimatis (2006) and Chong and Wray (2005) offer recent problem is successively reduced via three computational examples of the typical approach. Our approach is different layers: Proto-cognition (information filtering and clustering), primarily in terms of scale: rather than integrating individual Micro-cognition (memory retrieval modulated by expertise) algorithms, we map three cognitive-architecture-level and Macro-cognition (knowledge-based reasoning). The systems to each of the three functionalities outlined above. consequence of this design is that knowledge-based reasoning The methodological hypothesis motivating this approach is is used primarily for non-routine, novel situations; more that by integrating these complete, relatively mature familiar situations are handled by experience-based memory approaches, we can enable faster exploration of alternatives retrieval. Filtering and clustering improve overall scalability by reducing the elements to be considered by higher levels. in design space than approaches that integrate sub- The paper describes the design of the architecture, two components of existing cognitive architectures. If true, this prototype explorations, and evaluation and limitations. will enable more rapid and thorough evaluation of the three- level architecture than constructing de novo a new Introduction architecture designed around the functional capabilities. An obvious potential drawback is the potential Cognitive architecture research seeks to define a collection redundancy in functional capability at each level, with of integrated processes and knowledge representations that consequences for software engineering complexity and run- provide a general foundation for intelligent behavior across time performance. The architecture reported in this paper is an increasingly broad spectrum of problems. While most not the end-goal, but rather a description of the results of cognitive architectures seek to cover the requirements for our experiences along this methodological path. The results general intelligence, all today fall far short of that goal. suggest that even the superficial integrations we report here They represent “works in progress” that tend to have niche have significant functional value. However, long-term, our strengths in a few problem domains. goal is to refine and deepen the initial integrations described Investigations of human psychology suggest that human here, based on lessons learned from the explorations. behavior derives from the integration of three mechanisms: This paper reports the initial progress and lessons learned 1) powerful perceptual and “pre-cognitive” processing; 2) in terms of evaluating the methodological hypothesis. We memory, along with processes that contextualize and report progress toward three goals: 1) defining the generalize experience to make it readily applicable in future functional requirements for each level of the architecture, 2) evaluating the utility of the general design pattern via Tight Integration prototype implementations, and 3) identifying functional A second architectural principle for C3I1 is tight synergies that could result long-term in tighter integration. integration: fine-grained, full-spectrum interaction between In order to explore design consequences empirically, we the cognitive levels. Tight integration may seem like an have chosen specific candidate systems for each level of the unintuitive requirement, given the components to be system. Pre-cognitive processing, or Proto-cognition, is integrated. However, both Soar and ACT-R suggest that accomplished via configurations of swarming algorithms. tight integration results in better leveraging of the inherent ACT-R is used for memory formation and expertise-based power of individual components (Anderson et al. 2004; retrieval (Micro-cognition), serving as a bridge between the Jones and Wray 2006). With loose integration, information sub-symbolic Proto-cognitive component and a symbolic about the situation becomes “trapped” inside subsystems Macro-cognitive layer. Macro-cognition, the locus of and cannot be efficiently communicated. In a tightly symbolic reasoning and explicit knowledge, is realized via integrated approach, all cognitive components will share a Soar. Each instantiation was chosen because of its maturity common memory, integrated control process, and a shared and capability with respect to the practical realization of the language for communication with individual components. target functionality, rather than any regard to cognitive The neurological architecture of human cognition is plausibility or a definite commitment to a particular choice characterized by both forward and reverse projections from of method for any level. Other approaches to the same interconnected areas. This observation inspired a design functionality could be substituted, allowing for an choice that each information flow between cognitive layers evaluation of the properties of constituent mechanisms. would have a reverse learning flow. For example, even in the prototype we describe, Proto-cognition’s filtering and clustering are modulated by signals from Micro-cognition Design Principles indicating goodness-of-fit and applicability of high salience The design of C3I1 is inspired by two principles particularly clusters to on-going problem solving. This allows the important in the human cognitive architecture: progressive architecture to adapt and optimize itself to the nature of its filtering and structuring of perception (“cognitive pyramid”) processing and the structure of its environment. In this and tight integration of functionalities and modalities. paper, we focus on describing the implemented and tested version of the architecture. This prototype currently falls far The Cognitive Pyramid short of a tightly integrated architecture, but suggests There is a computational trade-off between the quantity of specific opportunities for beneficial, synergistic integrations data that can be processed at any one time and the as discussed in the conclusions. sophistication of reasoning applied to that data. The human cognitive architecture solves this problem by collapsing and C3I1 Implementation integrating large volumes of perceptual information into This section outlines the role of the three primary more abstract aggregates that can then be stored, retrieved, components of the architecture. Importantly, the individual manipulated and composed more tractably. We refer to this architectures proposed for each level are not unique choices; volume-complexity tradeoff as a “cognitive pyramid”. other architectures or methods likely could have been When the volume of incoming data is high, the architecture applied at each level. The focus instead concerns how these applies local processing to the data, the function of which is more general systems have been applied to the specific role to aggregate and filter. At progressively
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