• (2002) Jordi Delgado - Emergence of social conventions in complex networks

  Here, "social conventions" means a very specific property of graphs in the context of game theory. Their social networks are groups of mindless automotaons which each have a single opinion that can take the values "A" or "B". They use the "coordination game" payoff matrix that engourages each pair of agents to agree with each other, and study various ways the graph can come to 90% of the agents all believe either "A" or "B". It's probably not useful for actual social worlds, and there's no simulation of any interesting environment, but it might be useful for designing protocols, or as a problem solving method.

  References:

  • L.A. Nunes Amaral, A. Scala, M. Barthélémy, H.E. Stanley, Classes of small-world networks, Proc. Nat. Acad. Sci. 97 (2000) 11149–11152.
  • D.J Watts, S.H. Strogatz, Collective dynamics of small-world networks, Nature 393 (1998) 440–442.
  • Y. Shoham, M. Tennenholtz, On the emergence of social conventions: Modeling, analysis and simulations, Artificial Intelligence 94 (1997) 139–166.
• (1997) Yoav Shoham, Moshe Tennenholtz - On the emergence of social conventions: modeling, analysis, and simulations

Marcelo A. Falappa, Gabriele Kern-Isberner, Guillermo R. Simari - Explanations, belief revision and defeasible reasoning

Claudio Bettini, X.Sean Wang, Sushil Jajodia - Solving multi-granularity temporal constraint networks

Alberto Maria Segre, Sean Forman, Giovanni Resta, Andrew Wildenberg - Nagging: A scalable fault-tolerant paradigm for distributed search

Fahiem Bacchus, Xinguang Chen, Peter van Beek, Toby Walsh - Binary vs. non-binary constraints

Jie Cheng, Russell Greiner, Jonathan Kelly, David Bell, Weiru Liu - Learning Bayesian networks from data: An information-theory based approach

Kurt Engesser, Dov M. Gabbay - Quantum logic, Hilbert space, revision theory

J.-D. Fouks, L. Signac - The problem of survival from an algorithmic point of view

Catherine Carr - The MIT Encyclopedia of the Cognitive Sciences, edited by Robert Wilson and Frank Keil

• Tim Taylor - Christoph Adami, Introduction to Artificial Life References:
  • M.A. Boden (Ed.), The Philosophy of Artificial Life, Oxford University Press, Oxford, 1996.
  • C.G. Langton (Ed.), Artificial Life: An Introduction, MIT Press, Cambridge, MA, 1995.

A.S d'Avila Garcez, K Broda, D.M Gabbay - Symbolic knowledge extraction from trained neural networks: A sound approach

José Hernández-Orallo - Truth from Trash. How Learning Makes Sense by Chris Thornton

Fabio G. Cozman - Credal networks

Aaron N. Kaplan, Lenhart K. Schubert - A computational model of belief

Mike Perkowitz, Oren Etzioni - Towards adaptive Web sites: Conceptual framework and case study

Wilhelm Rödder - Conditional logic and the Principle of Entropy

Christian Vilhelm, Pierre Ravaux, Daniel Calvelo, Alexandre Jaborska, Marie-Christine Chambrin, Michel Boniface - Think!: A unified numerical–symbolic knowledge representation scheme and reasoning system

Charles L. Ortiz Jr. - A commonsense language for reasoning about causation and rational action

Raúl E. Valdés-Pérez - Principles of human—computer collaboration for knowledge discovery in science

Paul Snow - The vulnerability of the transferable belief model to Dutch books

Simon Kasif, Steven Salzberg, David Waltz, John Rachlin, David W. Aha - A probabilistic framework for memory-based reasoning

Geoffrey LaForte, Patrick J. Hayes, Kenneth M. Ford - Why Gödel's theorem cannot refute computationalism

Hiroshi Motoda, Kenichi Yoshida - Machine learning techniques to make computers easier to use

Aravind K. Joshi - Role of constrained computational systems in natural language processing

Moshe Tennenholtz - On stable social laws and qualitative equilibria

Michael Arbib - The metaphorical brains

Andrew Gelsey, Mark Schwabacher, Don Smith - Using modeling knowledge to guide design space search

Márk Jelasity, József Dombi - GAS, a concept on modeling species in genetic algorithms

Randall H. Wilson - Geometric reasoning about assembly tools

Kurt Ammon - An automatic proof of Gödel's incompleteness theorem

Shmuel Onn, Moshe Tennenholtz - Determination of social laws for multi-agent mobilization

Stuart J. Russell - Rationality and intelligence

Hidde de Jong, Arie Rip - The computer revolution in science: steps towards the realization of computer-supported discovery environments

Adnan Darwiche, Judea Pearl - On the logic of iterated belief revision

R.C. Holte, T. Mkadmi, R.M. Zimmer, A.J. MacDonald - Speeding up problem solving by abstraction: a graph oriented approach

R. Holte, T. Mkadmi, R.M. Zimmer, A.J. McDonald - Speeding up problem solving by abstraction: a graph oriented approach

Raúl E. Valdés-Pérez - A new theorem in particle physics enabled by machine discovery

Dan Roth - On the hardness of approximate reasoning

Bart Selman, David G. Mitchell, Hector J. Levesque - Generating hard satisfiability problems

Herbert A. Simon - Artificial intelligence: an empirical science

John K. Tsotsos - Behaviorist intelligence and the scaling problem

Shigeki Goto, Hisao Nojima - Equilibrium analysis of the distribution of information in human society

Raúl E. Valdés-Pérez - Machine discovery in chemistry: new results

Stephen W. Smoliar - Artificial life: Christopher G. Langton, ed.

Yoav Shoham, Moshe Tennenholtz - On social laws for artificial agent societies: off-line design

Barbara Hayes-Roth - An architecture for adaptive intelligent systems

Bruce Randall Donald - On information invariants in robotics

Ian P. Gent, Toby Walsh - Easy problems are sometimes hard

Tad Hogg, Colin P. Williams - The hardest constraint problems: A double phase transition

Yoram Moses, Yoav Shoham - Belief as defeasible knowledge

Donald Michie - Turing's test and conscious thought

John McDermott - R1 (“XCON”) at age 12: lessons from an elementary school achiever

Takeo Kanade - From a real chair to a negative chair

Harry G. Barrow, J.M. Tenenbaum - Retrospective on “Interpreting line drawings as three-dimensional surfaces”

Judea Pearl - Belief networks revisited

Glenn A. Kramer - A geometric constraint engine

Fausto Giunchiglia, Toby Walsh - A theory of abstraction

John L. Pollock - How to reason defeasibly

Aaron Sloman - The emperor's real mind: Review of Roger Penrose's the emperor's new mind: Concerning computers, minds and the laws of physics

Olivier Dordan - Mathematical problems arising in qualitative simulation of a differential equation

Eric Saund - Putting knowledge into a visual shape representation

Michael Freund, Daniel Lehmann, Paul Morris - Rationality, transitivity, and contraposition

Anthony S. Maida - Maintaining mental models of agents who have existential misconceptions

Henry A. Kautz, Bart Selman - Hard problems for simple default logics

Mark J. Stefik, Stephen Smoliar - Four reviews of The Society of Mind and a response

Michael G. Dyer - A society of ideas on cognition: Review of Marvin Minsky's The Society of Mind

Matthew Ginsberg - The society of mind: Marvin Minsky

George N. Reeke Jr - The society of mind: Marvin Minsky

Stephen W. Smoliar - The society of mind: Marvin Minsky

Marvin Minsky - Society of mind: A response to four reviews

Stephen W. Smoliar - How to build a person: A prolegomenon: John Pollock

David Makinson, Karl Schlechta - Floating conclusions and zombie paths: Two deep difficulties in the “directly skeptical” approach to defeasible inheritance nets

Donald A. Norman - Approaches to the study of intelligence

Rodney A. Brooks - Intelligence without representation

David Kirsh - Today the earwig, tomorrow man?

Douglas B. Lenat, Edward A. Feigenbaum - On the thresholds of knowledge

Jordan B. Pollack - Recursive distributed representations

R. Bhaskar, Anil Nigam - Qualitative physics using dimensional analysis

Don F. Beal - A generalised quiescence search algorithm

Kai-Fu Lee, Sanjoy Mahajan - The development of a world class Othello program

Helmut Horacek - Reasoning with uncertainty in computer chess

Jeff Shrager - Induction: Process of inference, learning and discovery: John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard (MIT Press, Cambridge, MA, 1986); 355 pages

Daniel S. Weld - The psychology of everyday things: Donald A. Norman, (Basic Books, New York, 1988); 257 pages, $19.95

John R. Anderson - A theory of the origins of human knowledge

G. Tesauro, T.J. Sejnowski - A parallel network that learns to play backgammon

G. Priest - Reasoning about truth

Donald Perlis - Truth and meaning

Daniel S. Weld - Women, fire, and dangerous things: George Lakoff, (University of Chicago Press, Chicago, IL, 1987); 614 pages, $29.95

Mark J. Stefik - On book reviews policy and process

Robert K. Lindsay - The science of the mind: Owen J. Flanagan, Jr., (MIT Press, Cambridge, MA, 1984); 290 pages

Sheila Rock - On machine intelligence: Donald Michie, 2nd ed. (Ellis Horwood, Chichester, United Kingdom, 1986); 265 pages, £29.95

Stephen W. Smoliar - Epistemology and cognition: A.I. Goldman, (Harvard University Press, Cambridge, MA, 1986); ix + 437 pages, $27.50

David Elliot Shaw - On the range of applicability of an artificial intelligence machine

Michael Gordon - Machine intelligence and related topics: An information scientist's weekend book: Donald Michie, (Gordon and Breach, New York, 1982); 328 pages, $57.75

Ryszard S. Michalski, Patrick H. Winston - Variable precision logic

Martin Herman, Takeo Kanade - Incremental reconstruction of 3D scenes from multiple, complex images

vision : June 8–11, 1987, London, United Kingdom

André Vellino - Artificial intelligence: The very idea: J. Haugeland, (MIT Press, Cambridge, MA, 1985); 287 pp.

Judea Pearl - Fusion, propagation, and structuring in belief networks

Daniel G. Bobrow - Scientific debate

Mark Stefik - The AI business: Commercial uses of artificial intelligence: P.H. Winston and K.A. Prendergast, (MIT Press, Cambridge, MA 1984); 324 pages, $15.95

Hans Berliner, Carl Ebeling - The SUPREM architecture: A new intelligent paradigm

Donna Reese - Artificial intelligence: P.H. Winston, (Addison-Wesley, Reading, MA, 2nd ed., 1984); 527 pages

Kenneth D. Forbus - Structure and interpretation of computer programs: H. Abelson and G.J. Sussman with J. Sussman, (MIT, Cambridge, 1985); 503 pages

Chia-Hoang Lee, Azriel Rosenfeld - Improved methods of estimating shape from shading using the light source coordinate system

Daniel G. Bobrow, Patrick J. Hayes - Artificial intelligence — Where are we?

Barbara J. Grosz - Natural-language processing

Johan De Kleer - How circuits work

G.D. Ritchie, F.K. Hanna - am: A case study in AI methodology

Douglas B. Lenat, John Seely Brown - Why am and eurisko appear to work

Elaine Kant - On the efficient synthesis of efficient programs

Randall Davis, Reid G. Smith - Negotiation as a metaphor for distributed problem solving

Patrick H. Winston - Learning new principles from precedents and exercises

Paul S. Rosenbloom - A world-championship-level Othello program

Tomas Lozano-Perez - Robotics

Tom M. Mitchell - Generalization as search

Dana S. Nau - The last player theorem

Hans J. Berliner - Backgammon computer program beats world champion

Gerald Jay Sussman, Guy Lewis Steele Jr. - Constraints—A language for expressing almost-hierarchical descriptions

Takeo Kanade - A theory of Origami world

Ria Follett - Synthesising recursive functions with side effects

John McCarthy - Circumscription—A form of non-monotonic reasoning

Michael A. Bauer - Programming by examples

Patrick H. Winston - Learning by creatifying transfer frames

Alan Bundy - Will it reach the top? Prediction in the mechanics world

Richard M. Stallman, Gerald J. Sussman - Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis

D. Marr - Artificial intelligence—A personal view

Berthold K.P. Horn - Understanding image intensities

F. Malloy Brown - Doing arithmetic without diagrams

Azriel Rosenfeld - The psychology of computer vision: Patrick Henry Winston (ed.) McGraw-Hill, New York, 1975, vi+282 pages, $19.50

R.C.T. Lee - On machine intelligence: D. Michie. Halstead Press, a division of John Wiley & Sons, 1974.

W.W. Bledsoe, Peter Bruell - A man-machine theorem-proving system

Gary G. Hendrix - Modeling simultaneous actions and continuous processes

Yoshiaki Shirai - A context sensitive line finder for recognition of polyhedra

Kenneth Mark Colby, Franklin Dennis Hilf, Sylvia Weber, Helena C Kraemer - Turing-like indistinguishability tests for the validation of a computer simulation of paranoid processes

Aaron Sloman - Interactions between philosophy and artificial intelligence: The role of intuition and non-logical reasoning in intelligence

Charles B. Callaway, James C. Lester - Narrative prose generation

Katja Markert, Udo Hahn

Understanding metonymies in discourse Metonymies are difficult enough to drive these people to use the context of the sentences around the metonymy to interpret it. They create a set of heuristics which interpret metonomies. The first is obvious violations of sentence rules, such as having a non-agent do something only an agent can do.

Another rule is that metonomyies should be more "apt", where it's more likely for a T.V. Screen to refer to the T.V. than a small button on the T.V., or a transistor.

Metonymies should be very difficult for current parsers to understand, and are good examples, since they are short and require context and common sense.

They have a dumb, ad-hoc "common sense database" that is dissapointing. It contains subclasses and has-a relations.

References:

• D.A. Cruse, On the transitivity of the part-whole relation, J. Linguistics 15 (1979) 29–38. good quotes:
  • We took the door off its hinges and went through it.
  • The house has a handle.sources

Kathleen R. McKeown, Steven K. Feiner, Mukesh Dalal, Shih-Fu Chang - Generating multimedia briefings: coordinating language and illustration

Varol Akman - Formalizing common sense: Papers by John McCarthy: V. Lifschitz, ed., (Ablex Publishing Corporation, Norwood, NJ, 1990); vi+256 pages, hardback, ISBN 0-89391-535-1 (Library of Congress: Q335.M38 1989)

Akira Shimaya - Interpreting non-3-D line drawings

Adam J. Grove - Naming and identity in epistemic logic part II: a first-order logic for naming

Luc Lismont, Philippe Mongin - A non-minimal but very weak axiomatization of common belief

on integration of natural language and vision processing

Russell Greiner - Learning by understanding analogies

H.Jaap van den Herik, Jos W.H.M. Uiterwijk, Jack van Rijswijck - Games solved: Now and in the future

Jonathan Schaeffer, H.Jaap van den Herik - Games, computers, and artificial intelligence

Peter A. Flach - On the state of the art in machine learning: A personal review

A.G. Cohn, D. Perlis - “Field Reviews”: A new style of review article for Artificial Intelligence

James Delgrande, Arvind Gupta, Tim Van Allen - A comparison of point-based approaches to qualitative temporal reasoning

Weixiong Zhang, Rina Dechter, Richard E. Korf - Heuristic search in artificial intelligence

Karen Sparck Jones - Information retrieval and artificial intelligence

Wolfram Burgard, Armin B. Cremers, Dieter Fox, Dirk Hähnel, Gerhard Lakemeyer, Dirk Schulz, Walter Steiner, Sebastian Thrun - Experiences with an interactive museum tour-guide robot

Minoru Asada, Hiroaki Kitano, Itsuki Noda, Manuela Veloso - RoboCup: Today and tomorrow—What we have learned

Margaret A. Boden - Creativity and artificial intelligence

Daniel G. Bobrow, J.Michael Brady - Artificial Intelligence 40 years later

Fangzhen Lin, Hector J. Levesque - What robots can do: robot programs and effective achievability

Melanie Mitchell - L.D. Davis, handbook of genetic algorithms

Russell Greiner, Adam J. Grove, Alexander Kogan - Knowing what doesn't matter: exploiting the omission of irrelevant data

W. Whitney, S. Rana, J. Dzubera, K.E. Mathias - Evaluating evolutionary algorithms

David S. Touretzky - Neural networks in artificial intelligence: Matthew Zeidenberg

Mark J. Stefik, Stephen W. Smoliar - The commonsense reviews

Peter Szolovits, Stephen G. Pauker - Categorical and probabilistic reasoning in medicine revisited

Daniel G. Bobrow - Artificial intelligence in perspective: a retrospective on fifty volumes of the Artificial Intelligence Journal

David Kirsh - Foundations of AI: The big issues

Hector J. Levesque - All I know: A study in autoepistemic logic

J.T. Schwartz, M. Sharir - A survey of motion planning and related geometric algorithms

Larry S. Davis, Azriel Rosenfeld - Cooperating processes for low-level vision: A survey

Hans J. Berliner - A chronology of computer chess and its literature

John McCarthy - Artificial intelligence: a paper symposium: Professor Sir James Lighthill, FRS. Artificial Intelligence: A General Survey. In: Science Research Council, 1973

Alfonso Gerevini, Jochen Renz - Combining topological and size information for spatial reasoning

John Slaney, Sylvie Thiébaux - Blocks World revisited

Wai K. Yeap, Margaret E. Jefferies - Computing a representation of the local environment

R.P. Loui - On the origin of objects: B.C. Smith's MIT Press, Cambridge, MA, 1996. $37.50 (cloth). $17.50 (paper). 440 pages. ISBN 0-262-69209-0

Tze Yun Leong - Multiple perspective dynamic decision making

Cristiano Castelfranchi - Modelling social action for AI agents

Luc Steels - The origins of syntax in visually grounded robotic agents

Sebastian Thrun - Learning metric-topological maps for indoor mobile robot navigation

John Haugeland - Body and world: a review of What Computers Still Can't Do: A critique of artificial reason (Hubert L. Dreyfus): (MIT Press, Cambridge, MA, 1992); liii + 354 pages, $13.95

David J. Musliner, Edmund H. Durfee, Kang G. Shin - World modeling for the dynamic construction of real-time control plans

Jozsef A. Toth - Reasoning agents in a dynamic world: The frame problem: Kenneth M. Ford and Patrick J. Hayes, eds., (JAI Press, Greenwich, CT, 1991); 290+xiv pages

Michael A. Arbib, Jim-Shih Liaw - Sensorimotor transformations in the worlds of frogs and robots

Ingemar J. Cox, John J. Leonard - Modeling a dynamic environment using a Bayesian multiple hypothesis approach

on integration of natural language and vision processing

Demetri Terzopoulos, Andrew Witkin, Michael Kass - Constraints on deformable models:Recovering 3D shape and nonrigid motion

Bruce R. Donald - A search algorithm for motion planning with six degrees of freedom

Yorick Wilks - Making preferences more active

Azriel Rosenfeld - B. Jähne, H. Haussecker, and P. Geissler, eds., Handbook of Computer Vision and Applications. 1. Sensors and Imaging. 2. Signal Processing and Pattern Recognition. 3. Systems and Applications

Thomas F. Stahovich, Randall Davis, Howard Shrobe - Qualitative rigid-body mechanics

Tzachi Dar, Leo Joskowicz, Ehud Rivlin - Understanding mechanical motion: From images to behaviors

Minoru Asada, Eiji Uchibe, Koh Hosoda - Cooperative behavior acquisition for mobile robots in dynamically changing real worlds via vision-based reinforcement learning and development

Thomas F. Stahovich, Randall Davis, Howard Shrobe - Generating multiple new designs from a sketch

Ernst D. Dickmanns - Vehicles capable of dynamic vision: a new breed of technical beings?

Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez - Solving the multiple instance problem with axis-parallel rectangles

Rajesh P.N. Rao, Dana H. Ballard - An active vision architecture based on iconic representations

John K. Tsotsos, Scan M. Culhane, Winky Yan Kei Wai, Yuzhong Lai, Neal Davis, Fernando Nuflo - Modeling visual attention via selective tuning

Roger Mohr, Boubakeur Boufama, Pascal Brand - Understanding positioning from multiple images

Andrew Zisserman, David Forsyth, Joseph Mundy, Charlie Rothwell, Jane Liu, Nic Pillow - 3D object recognition using invariance

Naresh C. Gupta, Laveen N. Kanal - 3-D motion estimation from motion field

Damian M. Lyons - Vision, instruction, and action: David Chapman, (MIT Press Cambridge, MA, 1991); 295 pages, $35.00, (paperback)

Yoshinori Suganuma - Learning structures of visual patterns from single instances

Dana H. Ballard - Animate vision

Raymond Reiter, Alan K. Mackworth - A logical framework for depiction and image interpretation

Ellen Lowenfeld Walker, Martin Herman - Geometric reasoning for constructing 3D scene descriptions from images

Michele Barry, David Cyrluk, Deepak Kapur, Joseph Mundy, Van-Duc Nguyen - A multi-level geometric reasoning system for vision

Alex P. Pentland - Shading into texture

Brady - Parallelism in Vision

Jon A. Webb, J.K. Aggarwal - Structure from motion of rigid and jointed objects

Michael Brady - Computer vision

Takeo Kanade - Recovery of the three-dimensional shape of an object from a single view

Rodney A. Brooks - Symbolic reasoning among 3-D models and 2-D images

H.K. Nishihara - Intensity, visible-surface, and volumetric representations

Thomas O. Binford - Inferring surfaces from images

Larry S. Davis, Azriel Rosenfeld - Cooperating processes for low-level vision: A survey

• (1980) Berthold K.P. Horn, Brian G. Schunck - Determining optical flow

  Optical flow is an estimation of the movement of brightness patterns. If the image is "smooth" then optical flow is also an estimate of the movement of objects in the image (projected onto the plane of the image). They get some fairly good results on some very contrived examples. Important point is that calculating optical flow involves a relaxation process where the velocities of regions of constant brightness are inferred from the velocities of the edges of those regions.

  This paper is a lead up to Horn's book, Robot Vision.

  Hexagonal sampling may be a good alternative to rectangular sampling.

  A reduced version of this algorithm is implemented in hardware in optical mice to great effect.

  • Hamming, R.W., Numerical Methods for Scientists and Engineers (McGraw-Hill, New York, 1962).
  • Limb, J.O. and Murphy, J.A., Estimating the velocity of moving images in television signals, Computer Graphics and Image Processing 4 (4) (1975) 311-327.
  • Mersereau, R.M., The processing of hexagonally sampled two-dimensional signals, Proc. of the IEEE 67 (6) (1979) 930-949.

• (1993) Berthold K.P. Horn, B.G. Schunck - “Determining optical flow”: a retrospective

  Very useful read where Horn criticies his previous paper.

  • Whishes that he distinguished "optical flow" form "motion field". "Optical flow" is an image property, whilc the "motion field" is the movement of objects in 3D space. "Optical flow" is a 2D vector field; the "motion field" is 3D.
  • Wished he made the limitations of his algorithm more clear.
  • His original paper didn't concern itself with flow segmentation, which is required to interpret real world images with objects and a background.
  • Thinks that the best thing about the original paper is that it introduced variational calculus methods into computer vision.

  References:

  • R. Courant and D. Hilbert, Methods of Mathematical Physics (Interscience, New York, 1937/1953).
  • D. Mart, Vision (Freeman, San Francisco, CA, 1982).
  • C.M. Thompson, Robust photo-topography by fusing shape-from-shading and stereo,Ph.D. Thesis, Mechanical Engineering Department, MIT, Cambridge, MA (1993).
  • K. Ikeuchi and B.K.P. Horn, Numerical shape from shading and occluding boundaries, Artif lntell. 17 (1981) 141-184.

Katsushi Ikeuchi, Berthold K.P. Horn - Numerical shape from shading and occluding boundaries

Andrew P. Witkin - Recovering surface shape and orientation from texture

Irwin Sobel - On calibrating computer controlled cameras for perceiving 3-D scenes

P.M. Will, K.S. Pennington - Grid coding: A preprocessing technique for robot and machine vision

M.B. Clowes - On seeing things

Claude R. Brice, Claude L. Fennema - Scene analysis using regions

• (1999) Kenneth D. Forbus, Peter B. Whalley, John O. Everett, Leo Ureel, Mike Brokowski, Julie Baher, Sven E. Kuehne - CyclePad: An articulate virtual laboratory for engineering thermodynamics

  Should learn about thermodynamics, and about "thermal cycles." http://www.qrg.northwestern.edu/projects/NSF/cyclepad/cyclepad.htm

  This system is more about expressing models and assumtions than automatically generating them, and as such is similiar to our "math language" idea.

  It's like a simple circuit modeller, and similar to Dylan's idea of an online circuit modeler.

  We found that if CyclePad did not do the “obvious” propagation in preference to interpolation, students trusted it less.

  It's too bad that the paper doesn't mention the shortcommings of the system.

  • J.O. Everett, Topological inference of teleology: Deriving function from structure via evidential reasoning, Artificial Intelligence 113 (1999) 149–202.
  • P. Hayes, Naive physics 1: Ontology for liquids, in: J. Hobbs, R. Moore (Eds.), Formal Theories of the Commonsense World, Ablex, Norwood, NJ, 1985.
  • P. Nayak, Automated modeling of physical systems, Ph.D. Thesis, Computer Science Department, Stanford University, 1992.
  • R.W. Haywood, Analysis of Engineering Cycles: Power, Refrigerating and Gas Liquefaction Plant, Pergamon Press, 1985.
  • R.M. Stallman, G.J. Sussman, Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis, Artificial Intelligence 9 (1977) 135–196.
    • Dylan should read this, since it concerns his online circuit analysis idea.