Layered Cognitive Architectures: Where Cognitive Science Meets Robotics

Although overlooked in recent years as tools for research, cognitive software architectures are designed to bring computational models of reasoning to bear on real-world physical systems. This paper makes a case for using the executives in these architectures as research tools to explore the connection between cognitive science and intelligent robotics. Cognitive Architectures Three-layer intelligent control architectures (Gat, 1998) are by now well-known and are taught in many graduate level AI courses (e.g., (Murphy, 2000)). Usually presented as a framework for organizing software, and reported as engineering tools for bringing AI to bear on robotic applications (e.g., (Bonasso et al., 2003; Bonasso et al., 1997b; Freed et al., 2004), an important aspect of these architectures is the fact that they were motivated to integrate computational models of reasoning with realworld control of physical devices. In particular, the executive layer serves as a syntactic and semantic differential between the representations at the top layer and those of the continuous low-level control. Our claim is that these executives, in combination with representative application simulations, provide a common proving ground for cognitive science and AI robotics research. The executive is shown in the organization of a threelayer cognitive architecture, in Figure 1. Typically, deep reasoning in the way of generative planning and scheduling executes at the top tier, predicting tasks required to achieve control objectives given an initial situation, and assigning to them available resources. The lowest tier transforms primitive actions into continuous control and monitoring activity for the hardware. The executive tier takes highlevel goals from the top tier (or from a human agent), decomposes them from a library of reactive-plans and ultimately transforms them into primitives for the bottom tier. Control processing is closed loop at all tiers, permitting execution monitoring and reactive re-planning and reconfiguration in response to changes in the environment or the robotic hardware. Gluing Cognition to Sensing and Action The three layers correspond to what Newell termed the neural band, the cognitive band and the rational band (Newell, 1990) . In Newell’s theory, the cognitive band has multiple levels of knowledge processing from symbol access to goal attainment. And indeed, the executives in layered cognitive architectures must perform this range of cognitive processing in order to translate the form and intent of the top level (or human requests) to the continuous activity of the bottom level, as well as interpret the sensory events from the bottom level in the context of the goals of the top level. One of the first of these executives was the RAPs system (Firby, 1989; Firby, 1999). A typical RAP procedure, shown below, defines behavior for using a camera to visually locate and then move to a human. The applicability of the behavior depends on the whether the human’s location is already known, in which case the use of the camera is not required, and on the availability of the camera resource if needed. Here we see the tie from a state-based goal to the sensing and acting functions of the robot, together with a first order conjunctive query. RAPs is now embodied in a larger framework including a concept memory and a language parser for use in dialogue management (Fitzgerald and Firby, 1998). Thus, it is not Low level control Hardware Hardware Low level control • Converts goals to sequences of continuous activities • Interprets sensing as events • Intermediate time constants • Continuous • Sense/act processes • Small time constants • Event driven • Computationally intensive • Large time constants