We describe the dimensions along which Facebook users tend to express themselves via status updates using the semi-automated text analysis approach, the Meaning Extraction Method (MEM). First, we examined dimensions of self-expression in all status updates from a sample of four million Facebook users from four English-speaking countries (the United States, Canada, the United Kingdom, and Australia) in order to examine how these countries vary in their self-expressions. All four countries showed a basic three-component structure, indicating that the medium is a stronger influence than country characteristics or demographics on how people use Facebook status updates. In each country, people vary in terms of the extent to which they use Informal Speech, share Positive Events, and discuss School in their Facebook status updates. Together, these factors tell us how users differ in their self-expression, and thus illustrate meaningful use cases for the product: Talking about what’s going on tends to be positive, and people vary in terms of the extent to which their status updates are short, slangy emotional expressions and topics regarding school. The specific words that define these factors showed subtle differences across countries: The use of profanity indicates fewer school words (but only in Australia), whereas the UK shows greater use of slang terms (rather than profanity) when speaking informally. The MEM also identified English-language dialects as a meaningful dimension along which the countries varied. In sum, beyond simply indicating topicality of posts, this study provides insight into how status updates are used for self-expression. We discuss several theoretical frameworks that could produce these results, and more broadly discuss the generation of theoretical frameworks from wholly empirical data (such as naturalistic Internet speech) using the MEM.

AIIDE 2010 was held October 11-13, 2010, at Stanford University ajacent to Palo Alto, California. The conference featured 17 paper presentations, 18 posters, 5 demos, 5 invited speakers, a panel on teaching game AI in academe, and the first StarCraft AI competition. Led by the conference chair, Michael Youngblood (University of North Carolina at Charlotte), and the program chair, Vadim Bulitko (University of Alberta), the three days of AIIDE contained a dense and exciting agenda highlighting new research and revealing how AI is applied in many commercial endeavors. The first day was kicked off with an invited talk from Chris Jurney, lead developer of Double Fine Productions, who detailed his work on the nonplayer character pathfinding of Dawn of War II during his time at Relic Entertainment. The morning was completed by research presentations on behavioral techniques with notable work on producing realistic behaviors through alibi generation (Ben Sunshine-Hill and Norman Badler, University of Pennsylvania), which has been widely discussed in the community since, and Ben Weber's (University of California, Santa Cruz) work applying goal-driven autonomy to playing StarCraft (awarded AIIDE 2010 Best Student Paper).

Transfer learning aims to solve new learning problems by extracting and making use of the common knowledge found in related domains. A key element of transfer learning is to identify structured knowledge to enable the knowledge transfer. Structured knowledge comes in different forms, depending on the nature of the learning problem and characteristics of the domains. In this article, we describe three of our recent works on transfer learning in a progressively more sophisticated order of the structured knowledge being transferred. We show that optimization methods, and techniques inspired by the concerns of data reuse can be applied to extract and transfer deep structural knowledge between a variety of source and target problems. In our examples, this knowledge spans explicit data labels, model parameters, relations between data clusters and relational action descriptions. 

See Leuski et al. (2006) and to the same question -- for example, "What Leuski and Traum (2008) for more details. is your name?" -- depending on who the interactor The final parameter is the classification threshold is looking at. NPCEditor's user interface allows the on the KL-divergence value: only answers that designer to define arbitrary annotation classes or score above the threshold value are returned from categories and specify which of these annotation the classifier. The threshold is determined by tuning categories should be used in classification.

This prize is awarded biennially to recognize and encourage outstanding artificial intelligence research advances that are made by using experimental (Max Planck Institute for Biological Nectar, as well as poster presentations methods of computer science. Cybernetics), Karrie Karahalios (University by a select number of exceptional Thrun and Whittaker, whose teams of Illinois), Michael Kearns technical papers, short papers, student won the 2005 DARPA Grand Challenge (University of Pennsylvania), and Kurt abstracts, and doctoral consortium abstracts. A special Joint will feature talks on five award-winning in particular for high-impact IAAI-11/AAAI-11 Invited Talk by deployed AI applications and 14 contributions to the field of artificial David Ferrucci (IBM T. J. Watson Research emerging applications. The week is intelligence through innovation and Center) on "Building Watson: filled with a host of other programs, achievement in autonomous vehicle An Overview of DeepQA for the ...

Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system's component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements.

Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. This short paper discusses the history and implications of learning g-costs, both alone and in conjunction with learning h-costs as an introduction to the new f-LRTA* algorithm which learns both.

Dijkstra's single-source shortest-path algorithm (DA) is one of the well-known, fundamental algorithms in computer science and related fields. DA is commonly taught in undergraduate courses. Uniform-cost search (UCS) is a simple version of the best-first search scheme which is logically equivalent to DA. In this paper I compare the two algorithms and show their similarities and differences. I claim that UCS is superior to DA in almost all aspects. It is easier to understand and implement. Its time and memory needs are also smaller. The reason that DA is taught in universities and classes around the world is probably only historical. I encourage people to stop using and teaching DA, and focus on UCS only.

T oyota T e hnolo gi al Institute at Chi ago 1427 East 60th Str e et, Chi ago IL, 60637 USA Abstra t Au tions are b e oming an in reasingly p opular metho d for transa ting business, esp e-ially o v er the In ternet. This arti le presen ts a general approa h to building autonomous bidding agen ts to bid in m ultiple sim ultaneous au tions for in tera ting go o ds. A ore omp onen t of our approa h learns a mo del of the empiri al pri e dynami s based on past data and uses the mo del to analyti ally al ulate, to the greatest exten t p ossible, optimal bids. W e in tro du e a new and general b o osting-based algorithm for onditional densit y estimation problems of this kind, i.e., sup ervised learning problems in whi h the goal is to estimate the en tire onditional distribution of the real-v alued lab el. This approa h is fully implemen ted as A TT a -2001, a tops oring agen t in the se ond T rading Agen t Comp etition (T A C-01). In an au tion for a single go o d, it is straigh tforw ...

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.