UTCS Artificial Intelligence
DISCERN is a large, modular neural network system for reading, paraphrasing and answering questions about stereotypical (script-based) stories. A precis of this book and a short summary paper should give you a quick overview of this research. To get an idea what the DISCERN programs are like (without having to first port them), take a look at the on-line demo. It runs remotely on cascais.cs.utexas.edu, with graphics display on your X11 screen. The DISCERN software consists of four components: 1. PROC for training and testing the backpropagation-based processing modules (for parsing, generation, and question answering), 2. HFM for training and testing the hierarchical feature maps that form the basis for the episodic memory, 3. DISLEX for training and testing the lexicon (lexical and semantic feature maps and associative connections between them), and 4. DISCERN, which is the integrated complete story processing model put together from the final results of the above three programs. Comments to email@example.com.
Ralph E. Hoffman
ralph hoffman [at] yale edu
risto [at] cs utexas edu
Modeling Acute and Compensated Language Disturbance in Schizophrenia
Uli Grasemann, Ralph Hoffman and Risto Miikkulainen, In
Proceedings of the 33rd Annual Meeting of the Cognitive Science Society
Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia
Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, and Risto Miikkulainen,
, Vol. 69 (2011), pp. 997--1005.
A Subsymbolic Model of Language Pathology in Schizophrenia
Uli Grasemann, Risto Miikkulainen, Ralph Hoffman, In
Proceedings of the 29th Annual Conference of the Cognitive Science Society
, pp. 311-316, Hillsdale, NJ 2007. Erlbaum.
Text and Discourse Understanding: The DISCERN System
Risto Miikkulainen, In
A Handbook of Natural Language Processing: Techniques and Applications for the Processing of Language as Text
, R. Dale, H. Moisl and H. Somers (Eds.), pp. 905--919, New York 2002.
Script-Based Inference And Memory Retrieval In Subsymbolic Story Processing
(1995), pp. 137-163.
Integrated Connectionist Models: Building AI Systems on Subsymbolic Foundations
Artificial Intelligence and Neural Networks: Steps Toward Principled Integration
Honavar, V., and Uhr, L. (Eds.) (1994), pp. 483--508.
Subsymbolic Natural Language Processing: An Integrated Model Of Scripts, Lexicon, And Memory
Risto Miikkulainen, , MIT Press, Cambridge, MA 1993. MIT Press.
DISCERN: A Distributed Artificial Neural Network Model Of Script Processing And Memory
Risto Miikkulainen, PhD Thesis, University of California. 334.
Processing Script-Based Stories: The DISCERN System
1990 - 1994
Storing Information on Maps: The Trace Feature Map Model
1990 - 1994
Neural Network Models of Schizophrenic Language
2003 - Present
Areas of Interest
Brain and Cognitive Disorders
Natural Language Processing (Cognitive)
A Subsymbolic Model of Schizophrenic Language