Chris J. Thornton

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Chris Thornton [1] Chris James Thornton,

a British computer scientist and lecturer in the Department of Informatics at the University of Sussex, and formerly a lecturer in the department of artificial intelligence at the University of Edinburgh. He holds a BA in economics, a M.Sc in computer science and a Ph.D. in artificial intelligence [2]. His research interests around information theory and cognitive science include machine learning, neural networks, data compression, computational creativity, the problem of induction, Kolmogorov complexity, sequence prediction, algorithmic music, analogy formation, minimum description length encoding, information refinement and formal concept analysis [3]. One of his lectures deals with game playing, Minimax, Negamax and Alpha-Beta [4].

Selected Publications#

[5]

1987 …#

• Chris J. Thornton (1987). Hypercuboid-Formation Behaviour of Two Learning Algorithms. IJCAI 1987
• Chris J. Thornton (1988). Links between Content and Information-Content. ECAI 1988 [6]
• Chris J. Thornton (1988). A Computational Model for the Data Compression Metaphor. AIMSA 1988

1990 …#

• Chris J. Thornton (1990). The Kink Representation for Exclusive-OR. International Neural Network Conference
• Chris J. Thornton (1992). Techniques in Computational Learning: An Introduction. Chapman & Hall
• Chris J. Thornton, Benedict Du Boulay (1992). Artificial Intelligence Through Search. Springer
• Chris J. Thornton (1996). Parity: The Problem that Won’t Go Away. AI 1996
• Chris J. Thornton (1999). What do Constructive Learners Really Learn? Artificial Intelligence Review, Vol. 13

2000 …#

• Chris J. Thornton (2000). Truth from Trash: How Learning Makes Sense. Bradford Books [7]
• Chris J. Thornton (2003). Indirect sensing through abstractive learning. Intelligent Data Analysis, Vol. 7, No. 3
• Chris J. Thornton (2006). Principled Exploitation of Behavioural Coupling. Journal of Experimental and Theoretical Artificial Intelligence, Vol. 18, No. 3
• Chris J. Thornton (2009). Self-redundancy in Music. Dagstuhl Seminar Proceedings: Computational Creativity, pdf
• Chris J. Thornton (2009). Representation Recovers Information. Cognitive Science, Vol. 33, No. 8

2010 …#

• Chris J. Thornton (2011). Naive Inference viewed as Computation. CogSci 2011, citeseerx
• Chris J. Thornton (2011). Is there any Need to Mention Induction? CogSci 2011, slides as pdf
• Chris J. Thornton (2012). Fuzzy Memory Theory and its Use in Cognitive Science. CogSci 2012
• Chris J. Thornton (2013). A New Way of Linking Information Theory with Cognitive Science. CogSci 2013, citeseerx
• Chris J. Thornton (2014). Infotropism as the underlying principle of perceptual organization. pdf
• Chris J. Thornton (2016). Predictive processing is Turing complete: A new view of computation in the brain. pdf, mp4
• Chris J. Thornton (2017). Predictive processing simplified: The infotropic machine. Brain and Cognition, Vol. 112, pdf, mp4

2020 …#

• Chris J. Thornton (2021). Extensional Superposition and Its Relation to Compositionality in Language and Thought. Cognitive Science, Vol. 45, No. 5

External Links#

• Chris Thornton Profile | University of Sussex
• Chris Thornton
• Machine Learning - Lecture 1: Introduction to the Topic
• Intelligent Systems Techniques (MSc) and Knowledge and Reasoning (UG)
• KR-IST - Lecture 5a Game playing with Minimax and Pruning

References#

1. ↑ Chris Thornton
2. ↑ Chris J. Thornton, Benedict Du Boulay (1992). Artificial Intelligence Through Search. Springer
3. ↑ Spring term 2011 : Previous COGS seminars : … : Centre for Cognitive Science (COGS) : University of Sussex
4. ↑ KR-IST - Lecture 5a Game playing with Minimax and Pruning
5. ↑ dblp: Chris Thornton, includes Chris Thornton, University of British Columbia
6. ↑ Information content from Wikipedia
7. ↑ Jean Hayes Michie (2001). Machine Learning and Light Relief: A Review of Truth from Trash. AI Magazine, Vol. 22 No. 4, pdf

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