Abstract: A computer implemented method for parsing a sensorimotor Event experienced by an Embodied Agent into symbolic fields of a WM event representation mapping to a sentence defining the Event is described the method including the steps of: attending a participant object; classifying the participant object; and making a series of cascading determinations about the Event, wherein some determinations are conditional on the results of previous determinations, wherein each determination sets a field in the WM event representation

Abstract: Embodiments described herein relate to a method of changing the connectivity of a Cognitive Architecture for animating an Embodied Agent, which may be a virtual object, digital entity, and/or robot, by applying Mask Variables to Connectors linking computational Modules. Mask Variables may turn Connectors on or off—or more flexibly, they may module the strength of Connectors. Operations which apply several Mask Variables at once put the Cognitive Architecture in different Cognitive Modes of behaviour.

Abstract: Computational structures provide Embodied Agents with memory which can be populated in real time from Experience, and/or or authored. Embodied Agents (which may be virtual objects, digital entities or robots) are provided with one or more Experience Memory Stores which influence or direct the behaviour of the Embodied Agents. An Experience Memory Store may include a Convergence Divergence Zone (CDZ), which simulates the ability of human memory to represent external reality in the form of mental imagery or simulation that can be re-experienced during recall. A Memory Database be generated in a simple, authorable way, enabling Experiences to be learned during live operation of the Embodied Agents or authored. Eligibility-Based Learning determines which aspects from streams of multimodal information are stored in the Experience Memory Store.

Abstract: Disclosed is a machine-learning model-based chunker (the “Sequencer”) that learns to predict the next element in a sequence and detects the boundary between sequences. At the end of a sequence, a declarative representation of the whole sequence is stored, together with its effect. The effect is measured as the difference between the system states at the end and at the start of the chunk. The Sequencer can be combined with a Planner that works with the Sequencer to recognize what plan a developing incoming sequence can be a part of and thus to predict the next element in that sequence. In embodiments where the effect of a plan is represented by a multi-dimensional vector, with different attentional weights placed on each dimension, the Planner calculates the distance between the desired state and the effects generated by individual plans, weighting its calculation by the attentional foci.