Projects tagged ‘ai’ and ‘artificialintelligence’

Project OverviewThe S-Space Package is a collection of algorithms for building Semantic Spaces. These algorithms process text corpora and map semantic representations for words onto high dimensional ... [More] vectors. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics. The research and development is being done by the Natural Language Processing group at UCLA led by David Jurgens and Keith Stevens, under the advisory of Dr. Michael Dyer. See the Getting Started page for a quick introduction on how to use the S-Space package, or see the Package Overview for information on the code and available features. GoalOur initial goal is to provide a uniform implementation for many common semantic space algorithms in order to facilitate research in semantic spaces and provide an accurate, reproducible way to compare different algorithms. Second, we aim to provide a comprehensive framework for researchers to easily develop new algorithms without having to replicate much of the shared software. For those looking to implement their own Semantic Space algorithm within the S-Space package, we recommend looking at the Introduction page. ContactQuestions and Comments can be sent to s-space-research-dev@googlegroups.com AlgorithmsWe are actively working on supporting the following algorithms. As additional time and resources allow, we will add further algorithms. See the downloads page for the current list of algorithms available in command-line executable form. Latent Semantic Analysis (LSA)T. K. Landauer and S. T. Dumais, "A solution to Plato’s problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge," Psychological Review, vol. 104, pp. 211–240, 1997. T. K. Landauer, P. W. Foltz, and D. Laham, "Introduction to Latent Semantic Analysis," Discourse Processes, no. 25, pp. 259–284, 1998. Random Indexing (also, with permutations)P. Kanerva, J. Kristoferson, and A. Holst, “Random indexing of text samples for latent semantic analysis,” in Proceedings of the 22nd Annual Conference of the Cognitive Science Society, L. R. Gleitman and A. K. Josh, Eds., 2000, p. 1036. M. Sahlgren, “Vector-based semantic analysis: Representing word meanings based on random labels,” in Proceedings of the ESSLLI 2001 Workshop on Semantic Knowledge Acquisition and Categorisation, Helsinki, Finland, 2001. M. Sahlgren, A. Holst, and P. Kanerva, “Permutations as a means to encode order in word space,” in Proceedings of the 30th Annual Meeting of the Cognitive Science Society (CogSci’08), 2008. Correlated Occurrence Analogue to Lexical Semantic (COALS)D. L. T. Rohde, L. M. Gonnerman, and D. C. Plaut, “An improved model of semantic similarity based on lexical co-occurrence,” Cognitive Science, 2009, submitted. Online. Available: http://www.cnbc.cmu.edu/plaut/papers/abstracts/RohdeGonnermanPlautSUB-CogSci.COALS.html Hyperspace Analogue to Language (HAL)C. Burgess and P. Conley, “Developing semantic representations for proper names,” in Cognitive Science Proceedings, LEA, 1998, pp. 185–190. C. Burgess and K. Lund, “Modelling parsing constraints with high- dimensional context space,” Language and Cognitive Processes, vol. 12, pp. 177–210, 1997. Latent Relational Analysis (LRA)P. D. Turney, “Similarity of semantic relations,” Computational Linguistics, vol. 32, no. 3, pp. 379–416, 2006. Bound Encoding of the Aggregate Language Environment (BEAGLE)M. N. Jones, W. Kintsch, and D. J. K. Mewhort, “High-dimensional semantic space accounts of priming,” Journal of Memory and Language, vol. 55, pp. 534–552, 2006. M. N. Jones and D. J. K. Mewhort, “Representing word meaning and order information in a composite holographic lexicon,” Psychology Review, vol. 114, pp. 1–37, 2007. Syntactic Co-occurrence (by Greffenstette)G. Grefenstette, Explorations in Automatic Thesaurus Discovery. Indiana University Press, 1994. License and RestrictionsThe S-Space software package is free software released under the GPL v. 2 license. See our license and restrictions page for full details. [Less]

What?This is an open source software for AI, Machine Learning algorithms benchmarking, written in Java with TCP interface and example agents in Java and Python. This software suits for research ... [More] , teaching and self-learning. Starting from very simple examples you have ability to dive into very complex tasks. JNI is going to be supported fairly soon. Why?Got boring one day from walking just the mazes and came up with idea to create a universal benchmark with simple interface and entertaining content. When?This project was started in March 2009. Mario AI Competition was hold @ ICE-GIC in London on August, 24th and CIG in Milano on September, 7th, 2009. Currently approaching and working on organizing AI Mario Championship 2010. Who?by Julian Togelius and Sergey Karakovskiy with participant of Tom Schaul and Jan Koutnik. Where?@ IDSIA [Less]

PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments ... [More] to test and compare your algorithms. It's the Swiss army knife for machine learning and neural networking. [Less]

A Reinforcement Learning library in Python Includes Q-learning, Sarsa and more.

A seira is an ordered set of elements for state representation. The purpose of this project is to provide an environment that supports extensive generalization for abstract experiments in ... [More] combinatorial game theory. The intended use of this environment is primarily in the field of artificial intelligence where agent heuristics are to be evaluated against human responses. [Less]

Costruzione di uno risolutore per il problema dell’orario scolastico con vincoli hard e soft basato su tecniche di CSP. -i sorgenti del software eventualmente sviluppato -una breve relazione che ... [More] contenga: -descrizione del problema e delle ipotesi adottate -l'algoritmo e le caratteristiche della soluzione -discussione la complessità/effficienza stimata -test (con i dati di test) comparativi sulle prestazioni [Less]

A general purpose collection of game engine components. It is intended that these components are able to be composed together to create a game and engine without much coder effort after the fact. We ... [More] will rely heavily on object orientation, and design patterns, and focus toward data driven configuration and composition of components within the organisation of the framework. [Less]

Este proyecto involucra el análisis, la prueba, y la implementación en el lenguaje JAVA de algoritmos de aprendizaje por refuerzo, orientado a la generación de nuevas acciones como combinación ... [More] aquellas definidas en el dominio de problema. Con estos objetivos, se intenta probar que la resolución de ciertos problemas puede ser más eficiente que si no se contara con el mecanismo de generación de acciones. [Less]

Artificial Intelligence program. Makes logical derivation, statistical inference. Works with propositional logicl and declarative programming.