318 projects tagged "Artificial Intelligence"
Updated 06 Aug 2012
QSMM
| Pop | 21.45 |
|---|---|
| Vit | 1.00 |
QSMM, the "QSMM State Machine Model", is a framework for development of non-deterministic intelligent state models and systems with spur-driven behavior. It includes low-level functions for generating optimal actions by the system and high-level functions for building multinode models. In a multinode model, nodes represent components of a system you develop which choose optimal actions using the framework and can correspond to entities external to the system and which behavior is to be learnt. A node can choose optimal actions based on a current node state which is either set manually by your program or is identified automatically by the framework. Probability profiles for a state transition matrix and an action emission matrix of the node can be specified using an assembler program with a user-defined instruction set.
Updated 28 Jun 2012
Aivengo
| Pop | 19.62 |
|---|---|
| Vit | 18.04 |
Aivengo is an automated trading system (ATS) which uses Digital Signal Processing and Artificial Intelligence tools like Artificial Neuronal Networks, Genetic Algorithms, Fuzzy Logic, and chaos theory to predict financial market behavior.
Updated 09 Jun 2012
FreeFuzzyTime
| Pop | 22.80 |
|---|---|
| Vit | 18.54 |
FreeFuzzyTime is a time reasoner based on Fuzzy Temporal Constraint Networks (FTCN), which treats fuzzy temporal information efficiently. It can be integrated into applications for diagnosis. This is especially important in areas like Intensive Care Units, where patients' data are handled by a temporal database. FuzzyTime uses a structure which consists of three levels of abstraction. The upper layer is the user interface, where a translator transforms the expressions introduced by the user into temporal relations between temporal entities (points and intervals). The semantics of a user’s expressions are analyzed and stored in the intermediate layer, or temporal world. Finally, the bottom layer is based on the FTCN model.
Updated 17 Nov 2011
Wintermute
| Pop | 44.18 |
|---|---|
| Vit | 24.45 |
Wintermute is an intelligent framework of applications and libraries that uses neural networking to learn about its host. A pseudo-langauge engine that permits translations and grammar rulesets of any language to be incorporated into the system, and database downloads of different sets of data combine to provide a virtual self-thinking assistant that can be used to perform tasks like dictation to a text editor, and more complex tasks such as sorting of documents depending on the time of day, or automation of other routine tasks. It should be noted that Wintermute itself is a meta-project. It encompasses a large array of currently existing and potential produced projects.
Updated 17 Jul 2011
D* Lite
| Pop | 43.44 |
|---|---|
| Vit | 25.92 |
D* Lite is a C++ implementation of the D* Lite algorithm. D* Lite is an incremental heuristic search algorithm by Sven Koenig and Maxim Likhachev that builds on LPA*, an incremental heuristic search algorithm that combines ideas of A* and Dynamic SWSF-FP.
Updated 18 Jul 2011
Proper nouns
| Pop | 17.94 |
|---|---|
| Vit | 25.97 |
Proper nouns is a PHP class that can extract proper nouns from texts. It takes a text string and can detect which words may be proper nouns of people or other entities. It uses some heuristics like the capitalization of the first letter of a word, the presence of a person's title preceding the nouns, etc. The class may consider consecutive proper names as a single proper name. The class assumes English by default but may be configured to work with other idioms.
Updated 28 May 2011
Evolving Objects
| Pop | 43.13 |
|---|---|
| Vit | 26.88 |
EO is a template-based, ANSI-C++ evolutionary computation library that helps you to write your own stochastic optimization algorithms quickly. Evolutionary algorithms form a family of algorithms inspired by the theory of evolution, and solve various problems. They evolve a set of solutions to a given problem in order to produce the best results. These are stochastic algorithms because they iteratively use random processes. The vast majority of these methods are used to solve optimization problems, and may be also called "metaheuristics". They are also ranked among computational intelligence methods, a domain close to artificial intelligence. With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems, from continuous to combinatorial ones.
Updated 17 May 2011
ATRACO Project
| Pop | 21.86 |
|---|---|
| Vit | 27.07 |
The ATRACO Project is a prototype implementation of a trusted ambient ecology system that runs and manages activity spheres in an Ambient Intelligence Space. Activity spheres are realized by automatically discovering, selecting, and adapting smart devices (artefacts) existing in the space, according to user's preferences, customs, and activities. OWL ontologies are used for modeling user profile, devices, activities, and goal descriptions. Abstract plans are bound to specific devices, methods, and values through semantic matching.
Updated 28 Mar 2011
ca-ga
| Pop | 30.82 |
|---|---|
| Vit | 28.00 |
ca-ga is a toy artificial life simulation that uses genetic algorithms on large cellular automata. It uses simple but easily extended DNA that is 8k long by default, though you can take the size out to anything you have time to evolve. It sits under each cell of a 128x128 board and orders operations to transfer energy in the hopes of achieving a kill and breed. The simulation features a mutating fitness function, emergent sex, and a proof of concept real world fitness function. After enough generations, the cells or genes could achieve collectivism and organismhood, coordinating the values of the hotspots that determine board temperature in order to maintain a desired equilibrium. But maybe not. If you work in a fitness function, an optimizing problem solver results.
Updated 15 Oct 2012
Fuzzy machine learning framework
| Pop | 64.16 |
|---|---|
| Vit | 3.58 |
Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.
