- Title
- A model of heteroassociative memory: deciphering surprising features and locations
- Creator
- Bhatia, Shashank; Chalup, Stephan K.
- Relation
- Fourth International Conference on Computational Creativity (ICCC 2013). Proceedings of the Fourth International Conference on Computational Creativity (Sydney 12-14 June, 2013) p. 139-146
- Relation
- http://www.computationalcreativity.net/iccc2013/
- Publisher
- University of Sydney
- Resource Type
- conference paper
- Date
- 2013
- Description
- The identification of surprising or interesting locations in an environment is an important problem in the fields of robotics (localisation, mapping and exploration), architecture (wayfinding, design), navigation (landmark identification) and computational creativity. Despite this familiarity, existing studies are known to rely either on human studies (in architecture and navigation) or complex feature intensive methods (in robotics) to evaluate surprise. In this paper, we propose a novel heteroassociative memory architecture that remembers input patterns along with features associated with them. The model mimics human memory by comparing and associating new patterns with existing patterns and features, and provides an account of surprise experienced. The application of the proposed memory architecture is demonstrated by identifying monotonous and surprising locations present in a Google Sketchup model of an environment. An inter-disciplinary approach combining the proposed memory model and isovists (from architecture) is used to perceive and remember the structure of different locations of the model environment. The experimental results reported describe the behaviour of the proposed surprise identification technique, and illustrate the universal applicability of the method. Finally, we also describe how the memory model can be modified to mimic forgetfulness.
- Subject
- heteroassociative memory; human memory; robotics; architecture
- Identifier
- http://hdl.handle.net/1959.13/1317609
- Identifier
- uon:23452
- Identifier
- ISBN:9781742103174
- Language
- eng
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