Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
Optimal Ordered Problem Solver
Machine Learning
Rigid Flexibility: The Logic of Intelligence (Applied Logic Series)
Rigid Flexibility: The Logic of Intelligence (Applied Logic Series)
Reconciling situation calculus and fluent calculus
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On attention mechanisms for AGI architectures: a design proposal
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
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Systems intended to operate in dynamic, complex environments --- without intervention from their designers or significant amounts of domain-dependent information provided at design time --- must be equipped with a sufficient level of existential autonomy. This feature of naturally intelligent systems has largely been missing from cognitive architectures created to date, due in part to the fact that high levels of existential autonomy require systems to program themselves; good principles for self-programming have remained elusive. Achieving this with the major programming methodologies in use today is not likely, as these are without exception designed to be used by the human mind: Producing self-programming systems that can grow from first principles using these therefore requires first solving the AI problem itself --- the very problem we are trying to solve. Advances in existential autonomy call for a new programming paradigm, withself-programming squarely at its center. The principles of such a paradigm are likely to be fundamentally different from prevailing approaches; among the desired features for a programming language designed for automatic self-programming are (a) support for autonomous knowledge acquisition, (b) real-time and any-time operation, (c) reflectivity, and (d) massive parallelization. With these and other requirements guiding our work, we have created a programming paradigm and language called Replicode. Here we discuss the reasoning behind our approach and the main motivations and features that set this work from apart from prior approaches.