artificial-intelligence

A friend is using Factor Graphs to do text mining (identifying references to people in text), and it got me interested in this tool, but I'm having a hard time finding an intuitive explanation of what Factor Graphs are and how to use them. Can anyone provide an explanation of Factor Graphs that isn't math heavy, and which focusses on practical applications rather than abstract theory? They are used extensively for breaking down a problem into pieces. One very interesting application of factor graphs (and message passing on them) is the XBox Live TrueSkill algorithm. I wrote extensively about

Nominally a good problem to have, but I'm pretty sure it is because something funny is going on... As context, I'm working on a problem in the facial expression/recognition space, so getting 100% accuracy seems incredibly implausible (not that it would be plausible in most applications...). I'm guessing there is either some consistent bias in the data set that it making it overly easy for an SVM to pull out the answer, =or=, more likely, I've done something wrong on the SVM side. I'm looking for suggestions to help understand what is going on--is it me (=my usage of LibSVM)? Or is it the data?

What is the lastest in open source chatbot/fake AI 'technology' ? Is ELIZA/ALICE/MegaHAL still 'current', or have there been made any advances in the past decade ? ChatScript is the best open source tool that I've found for developing conversational agents (BSD License): https://github.com/ChatScript/ChatScript I have used it for several years and it's extremely programmer friendly (written in a way amenable to how programmers think and use tools). Most importantly, it was written to solve the truly messy problems of parsing natural language sentences with a powerful input pre-processor and a

I've read in one of my AI books that popular algorithms (A-Star, Dijkstra) for path-finding in simulation or games is also used to solve the well-known "15-puzzle". Can anyone give me some pointers on how I would reduce the 15-puzzle to a graph of nodes and edges so that I could apply one of these algorithms? If I were to treat each node in the graph as a game state then wouldn't that tree become quite large? Or is that just the way to do it? A good heuristic for A-Star with the 15 puzzle is the number of squares that are in the wrong location. Because you need at least 1 move per square that

Closed. This question is off-topic. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it's on-topic for Stack Overflow. I am really interested in AI and want to start programming in this field. What are the various areas within AI? e.g. Neural Networks etc. What book can be recommended for a beginner in AI and are there any preferred languages used in the field of AI? ThomasH Classical application areas of AI: Robotics Search Natural Language Processing Knowledge Representation / Expert Systems Planning / Scheduling Various algorithmic

I am looking for hexagonal self-organizing map on Python. ready module. If one exists. way to plot hexagonal cell algorithms to work with hexagonal cells as array or smth else About : A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) I don't have an answer for point 1, but some hints for point 2 and 3. In your context, you're not modelling a physical 2D space but a conceptual space with tiles that have 6 neighbors. This can be modelled with

I'm trying to devise an algorithm for a robot trying to find the flag(positioned at unknown location), which is located in a world containing obstacles. Robot's mission is to capture the flag and bring it to his home base(which represents his starting position). Robot, at each step, sees only a limited neighbourhood ( he does not know how the world looks in advance ), but he has an unlimited memory to store already visited cells. I'm looking for any suggestions about how to do this in an efficient manner. Especially the first part; namely getting to the flag. A simple Breadth First Search

Suppose we are given data in a semi-structured format as a tree. As an example, the tree can be formed as a valid XML document or as a valid JSON document. You could imagine it being a lisp-like S-expression or an (G)Algebraic Data Type in Haskell or Ocaml. We are given a large number of "documents" in the tree structure. Our goal is to cluster documents which are similar. By clustering, we mean a way to divide the documents into j groups, such that elements in each looks like each other. I am sure there are papers out there which describes approaches but since I am not very known in the area

When searching in a tree, my understanding of uniform cost search is that for a given node A, having child nodes B,C,D with associated costs of (10, 5, 7), my algorithm will choose C, as it has a lower cost. After expanding C, I see nodes E, F, G with costs of (40, 50, 60). It will choose 40, as it has the minimum value from both 3. Now, isn't it just the same as doing a Greedy-Search, where you always choose what seems to be the best action? Also, when defining costs from going from certain nodes to others, should we consider the whole cost from the beginning of the tree to the current node,