Hierarchical Skills and Cognitive Architectures

Hierarchical Skills and Cognitive Architectures Pat Langley (langley@csli.stanford.edu) Kirstin Cummings (kirstinc@ccrma.stanford.edu) Daniel Shapiro (dgs@stanford.edu) Computational Learning Laboratory, CSLI Stanford University, Stanford, CA 94305 Abstract Previous Research on Hierarchical Skills In this paper, we examine approaches to representing and utilizing hierarchical skills within the context of a cognitive architecture. We review responses to this issue by three established frameworks – ACT-R, Soar, and Prodigy – then present an alternative we have de- veloped within Icarus, another candidate architecture. Unlike most earlier systems, Icarus lets skills refer di- rectly to their subskills and communicate within a single recognize-act cycle. This assumption has implications for the number of cycles required to complete complex tasks. We illustrate our approach with the domain of multi-column subtraction, then discuss related methods and directions for future work in this area. A cognitive architecture (Newell, 1990) specifies the in- frastructure for an intelligent system, indicating those aspects of a cognitive agent that remain unchanged over time and across different application domains. Many proposals for the human cognitive architecture take the form of a production system, which stores long-term knowledge as a set of condition-action rules, encodes short-term elements as declarative list structures, and relies on a recognize-act cycle that matches against, and executes rules that alter, the contents of short-term memory. Soar, ACT-R, and EPIC are all examples of the production-system paradigm, although other archi- tectural frameworks are also possible. Despite the general agreement that cognitive skills are organized hierarchically, there exist different ways to im- plement this basic idea. Within a production-system ar- chitecture, the most natural scheme involves communi- cation between skills and their subskills through the ad- dition of elements to short-term memory. For instance, ACT-R achieves this effect using production rules that match against a generalized goal structure in their condi- tion sides, such as the desire to prove that two triangles are congruent, and, upon firing, create new subgoals, such as the desire to prove that two lines have the same length. This approach requires the explicit addition of goal elements to short-term memory, since this is the only mechanism through which other production rules can be accessed. Soar takes a somewhat different approach to encoding hierarchical skills that relies on multiple problem spaces. For instance, Jones and Laird (1997) report a detailed model of ying an aircraft in combat training scenarios. This system organizes its capabilities in a hierarchical manner, with each node implemented as a Soar problem space with associated states, operators, and goals. To invoke a lower-level problem space, the system adds a new element to short-term memory that refers to that space, and it must take similar action in order to exit. The details differ from those in ACT, but the passing of messages through short-term memory is similar. The Prodigy architecture (Minton et al., 1989) pro- duces cognitive behavior in yet another manner. The system represents knowledge about actions as Strips- like operators, and the central module utilizes means- ends analysis to decompose problems into subproblems. This gives Prodigy the ability to generate hierarchical Introduction and Overview Human skills are organized in a hierarchical fashion. There seems to be general agreement with this claim, as it is consistent not only with experimental findings about the execution and acquisition of skills, but also with introspection about our everyday behavior. Upon request, most people can describe their complex skills at successive levels of aggregation, whether these involve how they drive to work each day, how they cook a meal, or how they write a technical article. What remains an open question is how we should model such skill hierarchies in computational terms. Al- ternative approaches to modeling cognition encode the notion of hierarchy in distinct ways that have different implications for performance and learning. The most interesting positions are those which are embedded in theories of the human cognitive architecture, such as Soar (Laird et al., 1987), ACT-R (Anderson, 1993), and EPIC (Kieras & Meyer, 1997). These frameworks make strong commitments to both the mental representations of knowledge and to the processes that operate on them. In the pages that follow, we consider the challenge of modeling hierarchical skills within a unified theory of cognition. We begin with a brief review of three such ar- chitectures and their responses to this issue, then turn to Icarus, an alternative framework that approaches hier- archical skills from a different perspective. The key issue involves whether one can traverse levels of a hierarchy within a single cognitive cycle. Our illustrative example comes from a familiar cognitive skill that has a hierar- chical organization – multi-column subtraction – but we also consider other models that incorporate multi-level skills. We conclude by discussing related work and our plans to extend the architecture’s capabilities.