Engineering General Intelligence, Part 2

[1]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[2]  C. Flori,et al.  Homotopy Type Theory : Univalent Foundations of Mathematics , 2014 .

[3]  Risto Miikkulainen,et al.  Learning geometry from sensorimotor experience , 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

[4]  Meera Sitharam,et al.  Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction , 2011, BICA.

[5]  Lawrence B. Holder,et al.  Frequent subgraph mining on a single large graph using sampling techniques , 2010, MLG '10.

[6]  Rada Mihalcea,et al.  Unsupervised graph-based word sense disambiguation , 2009 .

[7]  Hoifung Poon,et al.  Unsupervised Semantic Parsing , 2009, EMNLP.

[8]  Xiaotong Zhang,et al.  Reasoning with Cardinal Directions: An Efficient Algorithm , 2008, AAAI.

[9]  Chris Cornelis,et al.  Fuzzy region connection calculus: Representing vague topological information , 2008, Int. J. Approx. Reason..

[10]  Chris Cornelis,et al.  Fuzzy region connection calculus: An interpretation based on closeness , 2008, Int. J. Approx. Reason..

[11]  Rada Mihalcea,et al.  Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity , 2007, International Conference on Semantic Computing (ICSC 2007).

[12]  Jürgen Schmidhuber,et al.  Gödel Machines: Fully Self-referential Optimal Universal Self-improvers , 2007, Artificial General Intelligence.

[13]  Pei Wang,et al.  Rigid Flexibility: The Logic of Intelligence , 2006 .

[14]  Wolfgang Wahlster,et al.  SmartKom: Foundations of Multimodal Dialogue Systems , 2006, SmartKom.

[15]  Nico Van de Weghe,et al.  A Qualitative Trajectory Calculus and the Composition of Its Relations , 2005, GeoS.

[16]  Leonardo Vanneschi,et al.  A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming , 2005, Evolutionary Computation.

[17]  Reza Shadmehr,et al.  The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning , 2004 .

[18]  Susan Foster-Cohen,et al.  CONSTRUCTING A LANGUAGE: A USAGE-BASED THEORY OF LANGUAGE ACQUISITION , 2004, Studies in Second Language Acquisition.

[19]  Murray Shanahan,et al.  A Logic-Based Formulation of Active Visual Perception , 2004, KR.

[20]  Xi Zhang,et al.  Top-down versus bottom-up learning in cognitive skill acquisition , 2004, Cognitive Systems Research.

[21]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[22]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[23]  Ian Mackie,et al.  Efficient Reductions with Director Strings , 2003, RTA.

[24]  Stephan Winter,et al.  Uncertain topological relations between imprecise regions , 2000, Int. J. Geogr. Inf. Sci..

[25]  Amos J. Storkey,et al.  The basins of attraction of a new Hopfield learning rule , 1999, Neural Networks.

[26]  Deniz Yuret,et al.  Discovery of linguistic relations using lexical attraction , 1998, ArXiv.

[27]  Corso Elvezia.  Probabilistic Incremental Program Evolution , 1997 .

[28]  Lee Spector,et al.  Simultaneous evolution of programs and their control structures , 1996 .

[29]  Daniel Dominic Sleator,et al.  Parsing English with a Link Grammar , 1995, IWPT.

[30]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[31]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[32]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..