AI agents combining natural language interaction, task planning, and business ontologies can help companies provide better-quality and more costeffective customer service. Our customer-service agents use natural language to interact with customers, enabling customers to state their intentions directly instead of searching for the places on the Web site that may address their concern. We use planning methods to search systematically for the solution to the customer's problem, ensuring that a resolution satisfactory for both the customer and the company is found, if one exists. Our agents converse with customers, guaranteeing that needed information is acquired from customers and that relevant information is provided to them in order for both parties to make the right decision. The net effect is a more frictionless interaction process that improves the customer experience and makes businesses more competitive on the service front.

Trends in Intelligent Information Knowledge Based Computer Systems. The 18th International FLAIRS Conference seeks high quality, original, Larry Holder, University of Texas at Arlington unpublished submissions in all areas of AI, including, but not limited to, holder@cse.uta.edu The FLAIRS conference offers a set of special tracks, and authors are encouraged to submit papers to a relevant track.

Critique is an application he best way to improve one's writing instructor, revise based on the feedback, that is comprised of a suite of programs and then repeat the whole process as often as that evaluate and provide feedback for errors in possible. Unfortunately, this puts an enormous grammar, usage, and mechanics, that identify load on the classroom teacher, who is faced the essay's discourse structure, and that recognize with reading and providing feedback for perhaps potentially undesirable stylistic features. The companion scoring application, e-rater version As a result, teachers are not able to give 2.0, extracts linguistically-based features writing assignments as often as they would from an essay and uses a statistical model of wish. For example, the singular indefinite determiner a is labeled with the part-of-speech symbol AT, the adjective good is tagged JJ, the singular common noun job gets the label NN. After the corpus is tagged, frequencies are collected for each tag and for each function word (determiners, prepositions, etc.), and also for each adjacent pair of tags and function words. The individual tags and words are called unigrams, and the adjacent pairs are the bigrams. To illustrate, the word sequence, "a good job" contributes to the counts of three bigrams: a-JJ, AT-JJ, JJ-NN, which represent, respectively, the fact that the function word a was followed by an adjective, an indefinite singular determiner was followed by a noun, and an adjective was followed by a noun.

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This model makes system debugging significantly easier, because we know We introduced a sensor abstraction layer to exactly what each sensor reading is at every separate the task layer from concerns about point in the computation; something that physical sensing devices. We process the sensor would not be the case if we were reading from information (from the laser rangefinder in this the sensors every time a reading was used in a application) into distance measurements from calculation. This model also allows us to inject the center of the robot, thus allowing consideration modified sensor readings into the system, as of sensor error models and performance described in the next section.

Sketch maps are an important spatial representation used in many geospatial-reasoning tasks. This article describes techniques we have developed that enable software to perform humanlike reasoning about sketch maps. We illustrate the utility of these techniques in the context of nuSketch Battlespace, a research system that has been successfully used in a variety of experiments. After an overview of the nuSketch approach and nuSketch Battlespace, we outline the representations of glyphs and sketches and the nuSketch spatial reasoning architecture. We describe the use of qualitative topology and Voronoi diagrams to construct spatial representations, and explain how these facilities are combined with analogical reasoning to provide a simple form of enemy intent hypothesis generation.

This eclectic keepsake provides a sampling was initially inspired by science fiction, "[iRobot Chairman Helen] Greiner believes'One of what can be found (with links to the full the movie may influence a new generation She said the R2D2 robot's humanlike She went on to the articles were initially available inventions were predicted by those sort of MIT where she earned undergraduate and online and without charge, few things that writers. In terms of the capabilities that graduate degrees in mechanical engineering, good last forever; and (4) the AI in the News we get in modern computers, they could electrical engineering and computer collection--updated, hyperlinked, and see some of that. What I find so interesting science. 'It takes all three (disciplines) and archived--can be found by going to is that we start with these ideas which they must all come together in robotics,' www.aaai.org/aitopics/html/current.html. June 10, "In the war on terror, University about robots programmed to think on Breazeal of the Massachusetts Institute of professor Robin Murphy finds herself a New Jersey.

We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of `query-by-humming' (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of {m error} or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications.

An accepted belief is a proposition considered likely enough by an agent, to be inferred from as if it were true. This paper bridges the gap between probabilistic and logical representations of accepted beliefs. To this end, natural properties of relations on propositions, describing relative strength of belief are augmented with some conditions ensuring that accepted beliefs form a deductively closed set. This requirement turns out to be very restrictive. In particular, it is shown that the sets of accepted belief of an agent can always be derived from a family of possibility rankings of states. An agent accepts a proposition in a given context if this proposition is considered more possible than its negation in this context, for all possibility rankings in the family. These results are closely connected to the non-monotonic 'preferential' inference system of Kraus, Lehmann and Magidor and the so-called plausibility functions of Friedman and Halpern. The extent to which probability theory is compatible with acceptance relations is laid bare. A solution to the lottery paradox, which is considered as a major impediment to the use of non-monotonic inference is proposed using a special kind of probabilities (called lexicographic, or big-stepped). The setting of acceptance relations also proposes another way of approaching the theory of belief change after the works of Gärdenfors and colleagues. Our view considers the acceptance relation as a primitive object from which belief sets are derived in various contexts.