Abstract
Despite the fact that there are thousands of programming languages existing there is a huge controversy about what language is better to solve a particular problem. In this paper we discuss requirements for programming language with respect to AGI research. In this article new language will be presented. Unconventional features (e.g. probabilistic programming and partial evaluation) are discussed as important parts of language design and implementation. Besides, we consider possible applications to particular problems related to AGI. Language interpreter for Lisp-like probabilistic mixed paradigm programming language is implemented in Haskell.
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References
Batischeva, V., Potapov, A.: Genetic programming on program traces as an inference engine for probabilistic. In: These AGI-15 Proceedings (to appear)
Futamura, Y.: Partial evaluation of computation process an approach to a compiler-compiler. Systems, Computers, Controls 2, 45–50 (1971)
Goodman, N.D., Stuhlmüller, A.: The design and implementation of probabilistic programming languages (retrieved on 2015/3/30). http://dippl.org
Goodman, N.D., Tenenbaum, J.B.: Probabilistic models of cognition (retrieved on 2015/3/30). http://probmods.org
Goodman, N., Mansinghka, V., Roy, D., Bonawitz, K., Tarlow, D.: Church: a language for generative models. In: Proc. 24th Conf. Uncertainty in Artificial Intelligence (UAI), pp. 220–229 (2008)
Jones, C., Plotkin, G.D.: A probablistic powerdomain of evaluations. In: Proceedings of Fourth Annual Symposium on Logic in Computer Science, pp. 186–195. IEEE Computer Society Press (1989)
Jones, N., Gomard, C., Sestoft, P.: Partial Evaluation and Automatic Program Generation. Prentice Hall (1994)
Kahn, K.: Partial evaluation, programming methodology, and artificial intelligence. AI Magazine 5, 53–57 (1984)
Khudobakhshov, V.: Metacomputations and program-based knowledge representation. In: Kühnberger, K.-U., Rudolph, S., Wang, P. (eds.) AGI 2013. LNCS, vol. 7999, pp. 70–77. Springer, Heidelberg (2013)
Kowalski, J., Szykuła, M.: Game description language compiler construction. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS, vol. 8272, pp. 234–245. Springer, Heidelberg (2013)
Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General game playing: game description language specification. Tech. rep., Stanford Logic Group Computer Science Department Stanford University, Technical Report LG-2006-01 (2008)
Potapov, A., Batischeva, V., Rodionov, S.: Optimization framework with minimum description length principle for probabilistic programming. In: These AGI-15 Proceedings (to appear)
Potapov, A., Rodionov, S.: Making universal induction efficient by specialization. In: Goertzel, B., Orseau, L., Snaider, J. (eds.) AGI 2014. LNCS, vol. 8598, pp. 133–142. Springer, Heidelberg (2014)
Wingate, D., Stuhlmüller, A., Goodman, N.D.: Lightweight implementations of probabilistic programming languages via transformational compilation. In: Proc. of the 14th Artificial Intelligence and Statistics (2011)
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Khudobakhshov, V., Pitko, A., Zotov, D. (2015). Programming Languages and Artificial General Intelligence. In: Bieger, J., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science(), vol 9205. Springer, Cham. https://doi.org/10.1007/978-3-319-21365-1_30
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DOI: https://doi.org/10.1007/978-3-319-21365-1_30
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