SPARK AND THE DYNAMIC BEHAVIORAL ARCHITECTURE OF AUTONOMOUS ROBOTS

Abstract

Autonomous robotics has traditionally been concerned with the ability of a machine to perceive its environment, make decisions, and act without continuous human intervention. Classical autonomous systems may react to changing circumstances, select among alternative actions, navigate uncertain environments, and modify their behavior according to sensory information. Behavior-based robotics further demonstrated that useful intelligent behavior may arise through continuous interaction between perception and action rather than through a single centralized representation of the world [1]. Modern robotics has subsequently developed increasingly sophisticated architectures for perception, planning, learning, control, and human–robot interaction [2]. Nevertheless, autonomy does not necessarily imply that the purpose of the robot itself is autonomously generated. In many autonomous systems, a mission, task, objective, reward structure, or operational role exists before the autonomous decision process begins. The robot may determine how, when, and under what circumstances to act, while the general reason for acting remains externally established. Developmental robotics and intrinsic-motivation research have substantially expanded this picture by investigating systems capable of exploration, curiosity-driven learning, skill acquisition, and self-generated goals [3–5]. Autotelic-agent research goes further by considering agents capable of representing, generating, selecting, and solving their own problems rather than operating exclusively within externally specified individual tasks [6]. These developments raise a more fundamental behavioral question: what causes a possible behavior or goal to emerge in the first place? Existing work on developmental value systems has shown that accumulated experience, intrinsic motivation, attention, acquired values, and innate or initialized values can influence future robotic behavior [7]. However, the present paper approaches the problem from a different engineering direction. Rather than beginning with a prescribed objective or immediately defining a reward or optimization function, the objective is to describe the continuously changing conditions under which an internal possibility may become behaviorally significant. Consider two robots. The first is an autonomous combat robot assigned a mission. Its environment may change continuously, and the robot may independently determine its immediate actions. Nevertheless, the general mission precedes those decisions: IMPOSED PURPOSE → CHANGING CONDITIONS → AUTONOMOUS DECISION → ACTION Now consider a robot that encounters a tennis court and develops the initiative to play tennis, although no human has issued the command play tennis. If the possibility of playing tennis arises through the interaction of the robot's accumulated experience, internal state, external circumstances, location, history, and available behavioral knowledge, the direction of causality is different: CONTINUOUSLY UPDATED STATE → EMERGING POSSIBILITY → CANDIDATE GOAL → AUTONOMOUS ACTION The distinction is therefore not simply between a non-autonomous and an autonomous robot. Both robots may possess substantial autonomy. The distinction concerns the origin of behavioral direction. In the first case, autonomy operates under an imposed purpose. In the second, an initiative or candidate purpose may itself emerge from the evolving behavioral state of the robot. This distinction is consistent with the central question developed in this paper: a target need not always be an initial condition; under some circumstances it may become an output of the behavioral process. To investigate this problem, the paper introduces the concept of a SPARK. SPARK is not initially assumed to be a particular equation, threshold, optimization criterion, or stochastic event. It denotes the presently unresolved event through which continuously changing internal and external conditions become sufficiently significant to initiate a new behavioral possibility. The distinction between matching, SPARK, and decision is deliberately maintained: matching describes compatibility among conditions, SPARK initiates a potentially meaningful new process, and decision determines whether the resulting candidate state or action is ultimately accepted. The proposed architecture is dynamic. Internal and external variables, their relative weights, and their matching relationships may depend upon both the state and history of the agent and the time and location of an event. Matching itself need not be restricted to instantaneous values; compatibility of rates of change and higher-order changes may also carry behavioral information. Accordingly, function, first-derivative, and second-derivative matching are treated as potentially independent dimensions rather than mandatory simultaneous conditions. The resulting behavioral state is therefore continuously evolving rather than represented by a fixed behavioral map. A practical architecture must also recognize that no robot requires every possible behavioral function. Robot classification determines the capacity of its behavioral library and consequently the internal and external variables that are available for behavioral evaluation. A single robot interacting with a human, for example, need not contain the same interaction functions as a cooperative multi-robot system. The architecture is therefore formulated as open: behavioral libraries may be extended, reduced, activated, or deactivated as the operational classification of the robot changes, without requiring reconstruction of the fundamental behavioral mechanism. The engineering problem considered in this paper can consequently be summarized by the following progression: ROBOT CLASSIFICATION → LIBRARY CAPACITY → INTERNAL/EXTERNAL VARIABLES → CONTINUOUS UPDATING → DYNAMIC MATCHING → BEHAVIORAL LANDSCAPE → SPARK → CANDIDATE GOAL / INITIATIVE → ACTION The purpose of the paper is not to claim that the complete mechanism of autonomous goal formation has been solved. Nor is it to prescribe detailed behavioral functions for every possible robot classification. Instead, the objective is to establish an open dynamic behavioral architecture in which the origin, evolution, and possible emergence of behavioral initiative can be studied as an engineering problem.