Tech Briefs

Algorithm Would Enable Robots to Solve Problems Creatively

A control architecture is based on hypotheses concerning natural intelligence.

A control architecture and algorithms to implement the architecture have been conceived to enable a robot to learn from its experiences and to combine knowledge gained from prior experiences in such a way as to be able to solve new problems. The architecture is an abstraction of an interacting system of relatively simple components that, when properly interconnected, should enable the spontaneous emergence of behaviors from the complete system that would not necessarily be expected from the individual components. These emergent behaviors should enable a robot to interact robustly and intelligently with a complex, dynamic environment.

A control architecture and algorithms to implement the architecture have been conceived to enable a robot to learn from its experiences and to combine knowledge gained from prior experiences in such a way as to be able to solve new problems. The architecture is an abstraction of an interacting system of relatively simple components that, when properly interconnected, should enable the spontaneous emergence of behaviors from the complete system that would not necessarily be expected from the individual components. These emergent behaviors should enable a robot to interact robustly and intelligently with a complex, dynamic environment.

The architecture represents a set of parallel distributed computational modules (software objects) that communicate with each other through message passing. The principal objects loosely correspond to the (presumed) common computational modules of mammalian brains. A set of sensory processing modules continually updates a spatio-temporally indexed short-term memory structure denoted the sensory ego-sphere (SES). Depending on the task context, an attentional mechanism determines the saliency of incoming sensory information. In response to changes in the state of motion of the robot, the time series of sensory information is partitioned into episodes. Episodes are encoded in vector form and stored in a database, denoted the database associative memory (DBAM), that comprises the long-term memory of the robot.

The DBAM has the implicit mathematical structure of a linear vector space that is constructed as the direct sum of three subspaces. One subspace encodes a change in motor state (a motor event). Another subspace encodes the state of the sensors immediately prior to the motor event — in other words, the sensory preconditions of the motor event. The third subspace encodes the sensory state subsequent to the motor event (sensory post-conditions). The vectors in the DBAM and the external events from which they are formed are called sensory-motor coordination (SMC) events.

Several research projects have demonstrated that over time, as a robot senses and acts, clusters form within a vector space of SMC descriptors. Categories emerge. Thus, it becomes possible to select an exemplar that describes an equivalence class of SMC events. The encoding of SMC events as motor events with sensory preconditions and sensory post-conditions makes it possible to use simple vectorspace distances as measures of dissimilarity or similarity of complete SMC events. Vector-space distances can also serve as measures of dissimilarity or similarity between the sensory post-conditions of one event and the sensory preconditions of another.