Episodic Memory for Cognitive Robots in Dynamic, Unstructured Environments

dc.contributor.advisor Sammut, Claude Flanagan, Colm 2022-02-18T04:23:54Z 2022-02-18T04:23:54Z 2022
dc.description.abstract Elements from cognitive psychology have been applied in a variety of ways to artificial intelligence. One of the lesser studied areas is in how episodic memory can assist learning in cognitive robots. In this dissertation, we investigate how episodic memories can assist a cognitive robot in learning which behaviours are suited to different contexts. We demonstrate the learning system in a domestic robot designed to assist human occupants of a house. People are generally good at anticipating the intentions of others. When around people that we are familiar with, we can predict what they are likely to do next, based on what we have observed them doing before. Our ability to record and recall different types of events that we know are relevant to those types of events is one reason our cognition is so powerful. For a robot to assist rather than hinder a person, artificial agents too require this functionality. This work makes three main contributions. Since episodic memory requires context, we first propose a novel approach to segmenting a metric map into a collection of rooms and corridors. Our approach is based on identifying critical points on a Generalised Voronoi Diagram and creating regions around these critical points. Our results show state of the art accuracy with 98% precision and 96% recall. Our second contribution is our approach to event recall in episodic memory. We take a novel approach in which events in memory are typed and a unique recall policy is learned for each type of event. These policies are learned incrementally, using only information presented to the agent and without any need to take that agent off line. Ripple Down Rules provide a suitable learning mechanism. Our results show that when trained appropriately we achieve a near perfect recall of episodes that match to an observation. Finally we propose a novel approach to how recall policies are trained. Commonly an RDR policy is trained using a human guide where the instructor has the option to discard information that is irrelevant to the situation. However, we show that by using Inductive Logic Programming it is possible to train a recall policy for a given type of event after only a few observations of that type of event.
dc.publisher UNSW, Sydney
dc.rights CC BY 4.0
dc.subject.other Episodic memory
dc.subject.other cognitive robotics
dc.subject.other unstructured environments
dc.subject.other topological mapping
dc.title Episodic Memory for Cognitive Robots in Dynamic, Unstructured Environments
dc.type Thesis
dcterms.accessRights open access
dcterms.rightsHolder Flanagan, Colm
dspace.entity.type Publication
unsw.relation.faculty Engineering School of Computer Science and Engineering School of Computer Science and Engineering
unsw.subject.fieldofresearchcode 400701 Assistive robots and technology
unsw.thesis.degreetype PhD Doctorate
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