WILLIAM: A Monolithic Approach to AGI

We present WILLIAM – an inductive programming system based on the theory of incremental compression. It builds representations by incrementally stacking autoencoders made up of trees of general Python functions, thereby stepwise compressing data. It is able to solve a diverse set of tasks including the compression and prediction of simple sequences, recognition of geometric shapes, write code based on test cases, self-improve by solving some of its own problems and play tic-tac-toe when attached to AIXI and without being specifically programmed for it.

[1]  Susumu Katayama.  Towards Human-Level Inductive Functional Programming , 2015, AGI.

[2]  Arthur Franz.  Some Theorems on Incremental Compression , 2016, AGI.

[3]  Arthur Franz,et al.  Artificial general intelligence through recursive data compression and grounded reasoning: a position paper , 2015, ArXiv.

[4]  Marcus Hutter,et al.  Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability (Texts in Theoretical Computer Science. An EATCS Series) , 2006 .

[5]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

[6]  Peter Elias,et al.  Universal codeword sets and representations of the integers , 1975, IEEE Trans. Inf. Theory.

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

[8]  Ray J. Solomonoff,et al.  Complexity-based induction systems: Comparisons and convergence theorems , 1978, IEEE Trans. Inf. Theory.

[9]  Jürgen Schmidhuber,et al.  Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.

[10]  Pierre Flener,et al.  An introduction to inductive programming , 2008, Artificial Intelligence Review.

[11]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

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

[13]  Laurent Orseau,et al.  Universal Knowledge-Seeking Agents for Stochastic Environments , 2013, ALT.

[14]  Emanuel Kitzelmann,et al.  Inductive Programming: A Survey of Program Synthesis Techniques , 2009, AAIP.

[15]  Riccardo Poli,et al.  Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications , 2008, Computational Intelligence: A Compendium.