If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the output of image search engines, e.g. Google Images, bypassing the need to manually collect large quantities of training data. This model can then be used to refine the quality of the image search, or to search through other sources of images. This integrated scheme has been implemented and optimized to be used in The Semantic Robot Vision Challenge as a new test-bed for research in the areas of image understanding and knowledge retrieval in large unstructured image databases.
This paper examines, by argument, the dynamics of sequences of behavioural choices made, when non-cooperative restricted-memory agents learn in partially observable stochastic games. These sequences of combined agent strategies(joint-policies) can be thought of as a walk through the space of all possible joint-policies. We argue that this walk, while containing random elements, is also driven by each agent's drive to improve their current situation at each point, and posit a learning pressure field across policy space to represent this drive. Different learning choices may skew this learning pressure, and affect the simultaneous joint learning of multiple agents.
In hand-sketched drawings, nearly identical strokes may have different meanings to a user. For instance, a scribble could signify either that a shape should be filled in or that it should be deleted. This work describes a method for determining user intention in drawing scribbles in the context of a pen-based computer sketch. Our study shows that given two strokes, a circle and a scribble, two features (bounding ratio and density) can quickly and effectively determine a user's intention.
Current feature-based methods for sketch recognition systems rely on human-selected features. Certain machine learning techniques have been found to be good nonlinear features extractors. In this paper, we apply a manifold learning method, kernel Isomap, with a new algorithm for multi-stroke sketch recognition, which significantly outperforms the standard featurebased techniques.
In this paper, we propose the use of a neuro-fuzzy strategy to develop a Web personalization framework for the dynamic suggestion of URLs retained interesting for the currently connected users. In particular, a hybrid strategy exploiting the combination of the fuzzy logic with the neural paradigm is proposed in order to discover useful knowledge from session data identified from the analysis of log files and represent it in a set of fuzzy rules expressed in an interpretable form.
The reasoner will produce the same answer to the same question, regardless of who the questioner is. However, since ontology reasoners have their facts distributed in the open internet environment, reasoners may contain "individualdependent" facts. Personalized reasoner is becoming possible. Furthermore, gathering "individual-dependent" facts from different individuals often contains inconsistent information or contradictions (Staab 2004). Manually solving the contradictions as usual is not a practical way out. Capability for personalized reasoner to reason with inconsistency should be developed. A personalized reasoner based on belief strengths of information sources is proposed in the paper. Key steps toward such a personalized reasoner are outlined below.