DiPRA: a layered agent architecture which integrates practical reasoning and sensorimotor schemas

levels, and a homogeneous action or plan selection mechanism. These systems, however, put even less emphasis on higher level cognitive processing. 1.1. Aims and structure of the paper The aim of this paper is to present an agent architecture, DiPRA (Distributed Practical Reasoning Architecture), which couples realistic embodiment with mechanisms of intentional action control that are more sophisticated than those offered by most robotic systems. DiPRA is a two-layer architecture. Higher level cognitive operations are realized in the intentional layer, whose design is inspired by Bratman et al.’s (1988) idea of practical reasoning, that is, a reasoning process that determines what one is to do, and steers action. Specifically, the higher layer realizes deliberation (i.e., the process of deciding what to do) and means-ends reasoning (i.e., the process of deciding how to do it), which jointly permit the selection of and planning of distal goals, or those not dictated by immediate affordances (Gibson 1979) in the environment. Moreover, it naturally implements replanning (changing plan to achieve the same goal), intention reconsideration (changing the intended goal), and commitment to intentions (pursuing intentions in the face of changing contextual conditions), which are fundamental abilities of practical reasoning systems. DiPRA includes also a sensorimotor layer composed of multiple sensorimotor schemas, roughly the equivalent of Norman and Shallice’s (1986) schemas. The sensorimotor layer is responsible for situated action in open-ended environments and, as we will discuss, is essential for ensuring grounding of knowledge manipulated at the higher layer. The rest of the paper is structured as follows: sec. 2 offers a conceptual analysis of the main challenges faced in designing multilevel control architectures, and how DiPRA’s design takes them into account. Sec. 3 describes the DiPRA architecture, and sec. 4 illustrates the implementation of a DiPRA agent: a simulated robot (with realistic embodiment) that plays the role of a ‘thief’ in a guards-and-thieves scenario. Specifically, in an artificial ‘house’ (a 3D realistic virtual environment) with two floors and nineteen rooms, the thief has to locate and steal a ‘treasure’ and at the same time escape from one or more ‘guards’; see fig. 6. Two experiments are described in sec. 4 which demonstrate DiPRA’s ability to balance among levels of control and to acquire grounded knowledge which is then used to guide further reasoning and deliberation. Then, in sec. 5 we discuss the solutions we adopted in the design of DiPRA in the light of experimental results, and in sec. 6 we draw our conclusions. 2. Willed and automatic control of action: challenges and possible solutions A schematic illustration of a generic layered architecture, which integrates willed and automatic levels of control, is provided in fig. 1. In the lower layer (automatic control) actions are selected and executed whose outcomes satisfy the agent’s active drives by exploiting current affordances in the environment or producing novel ones to be exploited successively. At the same time, sensory feedback is used for triggering further action selection as well as for action control. This process is sufficient for an automatic selection of action and the satisfaction of a basic repertoire of drives. The higher layer (willed control) is instead responsible for intentional action. Here a (practical) reasoning process is responsible for selecting the most adequate plan on the basis of active goals, background information, and novel knowledge (which is continuously extracted from February 16, 2009 13:59 Connection Science pezzulo-cs