Paper Info
Title
Artificial Motivations Based On Drive-Reduction Theory In Self-Referential Model-Building Control Systems
Abstract
Motivation and emotion are inseparable component factors of value systems in living beings, which enable them to act purposefully in a partially unknown and sometimes unforgiving environment. Value systems that drive innate reinforcement learning mechanisms have been identified as key factors in self-directed control and autonomous development towards higher intelligence and seem crucial in the development of a concept of "self" in sentient beings [1]. This contribution is concerned with the relationship between artificial learning control systems and innate value systems. In particular, we adapt the state-of-the-art model of motivational processes based on reduction of generalized drives towards higher flexibility, expressivity and representation capability. A framework for modelling self-adaptive value systems, which develop autonomously starting from an inherited (or designed) innate representation, within a learning control system architecture is formulated. We discuss the relationship of anticipated effects in this control architecture with psychological theory on motivations and contrast our framework with related approaches.
Year
Venue
Field
2015
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Architecture,Computer science,Model building,Drive reduction theory (learning theory),Artificial intelligence,Control system,Cognition,Psychological Theory,Machine learning,Reinforcement learning,Expressivity
DocType
ISSN
Citations 
Conference
2161-4393
1
PageRank 
References 
Authors
0.35
6
2
Name
Order
Citations
PageRank
Moritz Schneider172.19
Jürgen Adamy219239.49