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	ftp://ftp.cs.iastate.edu/pub/techreports/TR89-22/TR.ps.Z, 19910711 Protection from the Underspecified Gary T. Leavens and Jeannette M. Wing TR #96-04 April 1996 Keywords: Partial and total functions; Protective specifications; Specification languages; Underspecification; Larch. 1996 CR Categories: D.2.1 Requirements/ Specifications \| languages,
	ftp://ftp.cs.iastate.edu/pub/techreports/TR91-15/thesis.ps.Z, 19910722 Larch/Smalltalk: A Specification Language for Smalltalk by Yoonsik Cheon A Thesis Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE Major: Computer Science Approved: In Charge of Major Work For the Major Department For the Graduate College
	ftp://ftp.cs.iastate.edu/pub/techreports/TR94-07/TR94-07.ps.Z, 19940519 Tree-based Algorithm to Find the k-th Value in Distributed Systems Yoonsik Cheon and Johnny Wong TR #94-07 April, 1994 Keywords: searching k-th value, extrema finding, distributed algorithms, message passing, tree structures. 1992 CR Categories: C.2.4 Distributed
	ftp://ftp.cs.iastate.edu/pub/techreports/TR95-19/TR-app.ps.Z, 19950818 31 Chambers & Leavens Appendix A Correctness Theorems and Proofs This appendix gives formal correctness proofs for our typechecking algorithm. The proofs use the calculational format described by David Gries in his article Teaching Calculation and Discrimination: A More Effective Curriculum
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/flairs.consistency.thambu.93.ps, 19950905 Knowledge-base Consistency Maintenance in an Evolving Intelligent Advisory System Priyamvadha Thambu Department of Computer Science Iowa State University Ames, IA 50011 Vasant Honavar Dept. of Computer Science Iowa State University Ames, IA 50011 Thomas Bartay Dept. of Industrial & Manufacturing Systems
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/aaai93.ps, 19950905 Symbolic and Subsymbolic Learning for Vision: Some Possibilities Vasant Honavar Department of Computer Science Iowa State University Ames, IA 50011-1040, USA September 1, 1993
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/spie.janakiraman.93.ps, 19950905 Adaptive Learning Rate Selection for Backpropagation Networks Jayathi Janakiraman & Vasant Honavar Department of Computer Science 226 Atanasoff Hall Iowa State University Ames Iowa 50011-1040 September 11, 1993
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/gdml.ps, 19950905 Inductive Learning Using Generalized Distance Measures Vasant Honavar Department of Computer Science Iowa State University Ames, Iowa 50011. U.S.A. e-mail: honavar@iastate.edu Abstract1 This paper briefly reviews the two currently dominant paradigms in machine learning - the connectionist network (CN)
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/chen95-1.ps, 19950905 A Neural Memory Architecture for Content as Well as Address-Based Storage and Recall: Theory and Applications Chun-Hsien Chen & Vasant Honavar Artificial Intelligence Research Group CS TR #95-03 February 1995 i A Neural Memory Architecture for Content as well as Address-Based Storage and Recall: Theory
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/diagnosis.ps, 19950905 Intelligent Diagnosis Systems Karthik Balakrishnan & Vasant Honavar Artificial Intelligence Research Group Department of Computer Science Iowa State University Ames, Iowa 50011-1040 balakris@cs.iastate.edu, honavar@cs.iastate.edu
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/spartz.ps, 19950905 AN EMPIRICAL ANALYSIS OF THE EXPECTED SOURCE VALUES RULE Richard Spartz & Vasant Honavar Department of Computer Science Iowa State University Ames, IA 50010
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/lang.ps, 19950905 Toward Integrated Models of Natural Language Evolution, Development, Acquisition, and Communication in Multi-Agent Environments Vasant Honavar 1 Department of Computer Science Iowa State University Ames, Iowa honavar@cs.iastate.edu 1 Motivation The evolution of natural language in communities of
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/TR95-01.ps, 19950905 Evolutionary Design of Neural Architectures \| A Preliminary Taxonomy and Guide to Literature Karthik Balakrishnan & Vasant Honavar Artificial Intelligence Research Group CS TR #95-01 January 1995 1 Evolutionary Design of Neural Architectures \| A Preliminary Taxonomy and Guide to Literature Karthik
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/TR95-02.ps, 19950905 An Efficient Interactive Algorithm for Regular Language Learning TR95-02 Rajesh Parekh & Vasant Honavar February 6, 1995 Iowa State University of Science and Technology Department of Computer Science 226 Atanasoff Ames, IA 50011 An Efficient Interactive Algorithm for Regular Language Learning Rajesh
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/TR95-10.ps, 19950905 Quo Vadis - Adaptive Heuristics for Routing in Large Communication Networks Armin R. Mikler, Johnny S.K. Wong, Vasant Honavar Department of Computer Science Iowa State University Ames, Iowa 50011, USA e-mail: miklerjwongjhonavar@cs.iastate.edu
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/parekh95.ps, 19950905 An Efficient Interactive Algorithm for Regular Language Learning Rajesh Parekh & Vasant Honavar Artificial Intelligence Research Group CS TR #95-02 February 1995 i An Efficient Interactive Algorithm for Regular Language Learning Rajesh Parekh & Vasant Honavar Artificial Intelligence Research Group
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/fallsymp.abstract2.ps, 19950905 Symbolic and Subsymbolic Learning with Structured Representations for Vision Vasant Honavar Department of Computer Science 226 Atanasoff Hall Iowa State University Ames, Iowa 50011-1040 fax: (515) 294-0258 e-mail: honavar@iastate.edu Extended Abstract Submitted to the AAAI Fall Symposium on Machine
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/chen94-1.ps, 19950905 Neural Network Automata by Chun-Hsien Chen and Vasant Honavar Computer Science Department Iowa State University Ames, Iowa 50011
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/ainn94.ps, 19950905 Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards A Resolution of Dichotomy TR94-14 Vasant Honavar August 18, 1994 Iowa State University of Science and Technology Department of Computer Science 226 Atanasoff Ames, IA 50011 12 SYMBOLIC ARTIFICIAL INTELLIGENCE AND NUMERIC
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/connsci.ps, 19950905 BRAIN-STRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN Vasant Honavar and Leonard Uhr Computer Sciences Department University of Wisconsin-Madison
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/generate93.ps, 19950905 Generative Learning Structures and Processes for Generalized Connectionist Networks Vasant Honavar Department of Computer Science Iowa State University Leonard Uhr Computer Sciences Department University of Wisconsin-Madison
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/geom.ps, 19950905 Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms C-H Chen, R. G. Parekh, J. Yang, K. Balakrishnan, & V. Honavar Department of Computer Science 226 Atanasoff Hall, Iowa State University, Ames, IA 50011. U.S.A.
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/learn94.ps, 19950905 Toward Learning Systems That Integrate Different Strategies and Representations TR93-22 Vasant Honavar September 14, 1993 Iowa State University of Science and Technology Department of Computer Science 226 Atanasoff Ames, IA 50011 Toward Learning Systems That Integrate Different Strategies and
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/parekh92.ps, 19950905 AN EMPIRICAL COMPARISON OF FLAT-SPOT ELIMINATION TECHNIQUES IN BACK-PROPAGATION NETWORKS Rajesh Parekh, Karthik Balakrishnan, & Vasant Honavar Department of Computer Science Iowa State University. Ames, Iowa 50011
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/iwannt.mikler.93.ps, 19950905 Quo Vadis { A Framework for Adaptive Routing in Very Large Communication Networks Armin R. Mikler, Johnny S.K. Wong, Vasant G. Honavar Department of Computer Science Iowa State University Ames, Iowa 50011, USA mikler@cs.iastate.edu wong@cs.iastate.edu honavar@cs.iastate.edu
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/parekh93.ps, 19950905 Efficient Learning of Regular Languages Using Teacher-Supplied Positive Samples and Learner-Generated Queries TR93-25 Rajesh Parekh and Vasant Honavar October 19, 1993 Iowa State University of Science and Technology Department of Computer Science 226 Atanasoff Ames, IA 50011 Efficient Learning of
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/gannrep.ps, 19950905 Properties of Genetic Representations of Neural Architectures Karthik Balakrishnan and Vasant Honavar Department of Computer Science Iowa State University Ames, IA 50011. U.S.A
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/bal92.ps, 19950905 Faster Learning in Multi-Layer Networks by Handling Output-Layer Flat-Spots Karthik Balakrishnan and Vasant Honavar Department of Computer Science Iowa State University Ames - 50011 USA
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/TR95-18.ps, 19950905 A Neural Network Architecture for Syntax Analysis Chun-Hsien Chen & Vasant Honavar Artificial Intelligence Research Group Department of Computer Science Iowa State University Ames, Iowa 50011-1040 U.S.A chen@cs.iastate.edu, honavar@cs.iastate.edu ISU CS-TR 95-18, August 1995 1 A Neural Network
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/honuhr.ps, 19950905 Artificial Intelligence and Neural Networks: Steps Toward Principled Integration Leonard Uhr Computer Sciences Department University of Wisconsin Madison, Wisconsin 53706 Vasant Honavar Department of Computer Science Iowa State University Ames, IA 50011 June 16, 1994 1 Introduction The attempt to
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/control90.ps, 19950905 Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN) Vasant Honavar & Leonard Uhr Computer Sciences Department University of Wisconsin-Madison
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/mikler94-1.ps, 19950905 Quo Vadis - A Framework for Intelligent Routing in Large Communication Networks Armin R. Mikler, Johnny S.K. Wong, Vasant Honavar Department of Computer Science Iowa State University Ames, Iowa 50011, USA e-mail: miklerjwongjhonavar@cs.iastate.edu
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/yang-honavar.flairs.ps, 19950905 A Hierarchical Representation for Efficiently Learning of Three-Dimensional Object Recognition and Description Jihoon Yang & Vasant Honavar Department of Computer Science 226 Atanasoff Hall Iowa State University Ames, IA 50011-1040, USA March 11, 1993
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/TR95-11.ps, 19950905 A Neural Network Architecture for High-Speed Database Query Processing Chun-Hsien Chen & Vasant Honavar Department of Computer Science 226 Atanasoff Hall, Iowa State University, Ames, IA 50011. U.S.A.
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/bias92.ps, 19950905 Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns Vasant Honavar Department of Computer Science 226 Atanasoff Hall Iowa State University Ames, IA 50011-1040
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/mikler-sim.ps, 19950905 An Object-Oriented Approach to Modeling and Simulation of Routing in Large Communication Networks Armin R. Mikler, Johnny S.K. Wong, Vasant Honavar Department of Computer Science Iowa State University Ames, Iowa 50011, USA e-mail: miklerjwongjhonavar@cs.iastate.edu Keywords: Object-oriented simulation,
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/chen-95b.ps, 19950905 A Neural Network Architecture for a High-Speed Database Query Processing System Chun-Hsien Chen & Vasant Honavar Department of Computer Science 226 Atanasoff Hall, Iowa State University, Ames, IA 50011. U.S.A.
	ftp://ftp.cs.iastate.edu/pub/techreports/AI-Reports/hybrid94.ps, 19950905 Symbolic Artificial Intelligence, Connectionist Networks and Beyond TR94-16 Vasant Honavar and Leonard Uhr August 18, 1994 Iowa State University of Science and Technology Department of Computer Science 226 Atanasoff Ames, IA 50011 1 SYMBOLIC ARTIFICIAL INTELLIGENCE, CONNECTIONIST NETWORKS, AND BEYOND
	ftp://ftp.cs.iastate.edu/pub/techreports/TR93-28/TR.ps.Z, 19960721 An Operational Semantics of Firing Rules for Structured Analysis Style Data Flow Diagrams Gary T. Leavens, Tim Wahls, Albert L. Baker, and Kari Lyle TR #93-28d December 1993 (revised Dec. 1993, Sept. 1994, June, July 1996) Keywords: structured analysis, data flow diagram, operational semantics, formal
	ftp://ftp.cs.iastate.edu/pub/techreports/TR94-18/TR.ps.Z, 19960730 The Behavior-Realization Adjunction and Generalized Homomorphic Relations Gary T. Leavens and Don Pigozzi TR #94-18b September 1994, revised September 1994, July 1996 Keywords: behavior, realization, observable equivalence, simulation, generalized relation, abstract data type, model theory. 1994 CR
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-04/TR.ps.Z, 19961011 Protective Interface Specifications Gary T. Leavens and Jeannette M. Wing TR #96-04a April 1996, Revised October 1996 Keywords: Protective specifications; Specification languages; Underspecification; Partiality; Larch. 1996 CR Categories: D.2.1 Requirements/ Specifications \|
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-17/appendix.ps.Z, 19961217 50 BeCecil Chambers & Leavens Appendix A Dynamic Semantics The dynamic semantics of BeCecil is mainly concerned with the following domains: objects, inheritance relations, generic function cases, closures, storage tables, and contexts. Objects are characterized by their identity, their inheritance
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-17/main.ps.Z, 19961217 BeCecil, A Core Object-Oriented Language with Block Structure and Multimethods: Semantics and Typing Craig Chambers and Gary T. Leavens TR #96-17 December 1996 This report, minus the appendices, will appear in the proceedings of the The Fourth International Workshop on Foundations of Object-Oriented
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-17/appendix-A4.ps.gz, 19970123 50 BeCecil Chambers & Leavens Appendix A Dynamic Semantics The dynamic semantics of BeCecil is mainly concerned with the following domains: objects, inheritance relations, generic function cases, closures, storage tables, and contexts. Objects are characterized by their identity, their inheritance
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-17/main-A4.ps.gz, 19970123 BeCecil, A Core Object-Oriented Language with Block Structure and Multimethods: Semantics and Typing Craig Chambers and Gary T. Leavens TR #96-17 December 1996 This report, minus the appendices, will appear in the proceedings of the The Fourth International Workshop on Foundations of Object-Oriented
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-17/TR-A4.ps.gz, 19970123 BeCecil, A Core Object-Oriented Language with Block Structure and Multimethods: Semantics and Typing Craig Chambers and Gary T. Leavens TR #96-17 December 1996 This report, minus the appendices, will appear in the proceedings of the The Fourth International Workshop on Foundations of Object-Oriented
	ftp://ftp.cs.iastate.edu/pub/techreports/TR96-17/TR.ps.gz, 19970505 BeCecil, A Core Object-Oriented Language with Block Structure and Multimethods: Semantics and Typing Craig Chambers and Gary T. Leavens TR #96-17a December 1996, Revised April 1997 An earlier version of this report, minus the appendices, appeared in the proceedings of The Fourth International Workshop
	ftp://ftp.cs.iastate.edu/pub/techreports/TR97-22/TR.ps.gz, 19971205 Programming is Writing: Why Student Programs Need to be Carefully Read Gary T. Leavens, Albert L. Baker, Vasant Honavar, Steven M. LaValle, Gurpur Prabhu TR #97-23 December 1997 Keywords: software, programming, writing, student ratio, teaching assistant. 1994 CR Categories: K.3.2