The second Future of Humanity Institute on AGI and keynote was by Tecnalia neuroscientist Randal possible paths to technological singularity. Koene, who also gave a tutorial on the connection While the community of AGI researchers is between reinforcement learning models in AI and nowhere near a consensus on the best approach to in computational neuroscience. Koene's keynote the original, grand goal of the AI field, it's clear focused on technologies enabling detailed brain that the pursuit of the goal is alive and well, and imaging and whole-brain emulation and on the yielding interesting discoveries and discussions.

The Best Paper award went to Sidney D'Mello, Blair Lehman, and Natalie Person for "Expert Tutors' Feedback Is Immediate, Direct, and Discriminating" in the special track on Intelligent Tutoring Systems. The Best Student Paper award went to Rong Hu, Brian Mac Namee, and Sarah Jane Delany for "Off to a Good Start: Using Clustering to Select the Initial Training Set in Active Learning" in the general conference. The Best Poster award went to Robert Holder for "Problem Space Analysis for Library Generation and Algorithm Selection in Real-Time Systems" in the general conference. In addition to a diverse assortment of papers and British Columbia, who presented "What Should posters presented at the conference, FLAIRS-23 featured the World-Wide Mind Believe? Information about FLAIRS-24, University, who presented "Rational Ways of Talking"; including the call for papers, is available online at and Janet L. Kolodner of the Georgia Institute www.flairs-24.info. of Technology, who presented "How Can We Help Université de Paris-Sorbonne, who presented "Reasoning in Natural Language Using Combinatory

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, is pleased to present the 2010 Spring Symposium Series, to be held Monday through Wednesday, March 22–24, 2010 at Stanford University. The titles of the seven symposia are Artificial Intelligence for Development; Cognitive Shape Processing; Educational Robotics and Beyond: Design and Evaluation; Embedded Reasoning: Intelligence in Embedded Systems Intelligent Information Privacy Management; It’s All in the Timing: Representing and Reasoning about Time in Interactive Behavior; and Linked Data Meets Artificial Intelligence.

AAAI/SIGART Doctoral Consortium, and the second AAAI Educational Advances in Artificial Intelligence Symposium, to name only a few of the AAAI is pleased to present the 2011 Spring Symposium Series, to highlights. For complete information be held Monday through Wednesday, March 21-23, 2011, at on these programs, including Tutorial Stanford University.

The motivation for the project was to tackle what might be regarded as the "holy grail" of Internet search, replacing larger and larger numbers of keyword-based lists of links with perfect, direct answers to naturally phrased queries on any subject. The platform was also designed to scale, with the primary mechanism for answering more and more questions being the addition of knowledge to the platform rather than writing more program code. Additional knowledge areas are typically included by adding "knowledge about knowledge." The system is live and answers millions of questions per month, asked by real Internet users. Questions can be tried at (and API access obtained from) www.trueknowledge.com. All the intellectual External computer systems can connect to the property was subsequently transferred in 2006 platform at two points through an API.

We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted $t$-scores (cat scores). Second, for feature selection we propose thresholding cat scores by controlling false nondiscovery rates (FNDR). Third, training of the classifier is based on James--Stein shrinkage estimates of correlations and variances, where regularization parameters are chosen analytically without resampling. Overall, this results in an effective and computationally inexpensive framework for high-dimensional prediction with natural feature selection. The proposed shrinkage discriminant procedures are implemented in the R package ``sda'' available from the R repository CRAN.

The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior marginals and enhance its performance in an anytime manner. The scheme uses the cutset conditioning principle to tighten existing bounding schemes and to facilitate anytime behavior, utilizing a fixed number of cutset tuples. The accuracy of the bounds improves as the number of used cutset tuples increases and so does the computation time. We demonstrate empirically the value of our scheme for bounding posterior marginals and probability of evidence using a variant of the bound propagation algorithm as a plug-in scheme.

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in FRACTAL) in terms of a greedy approximation algorithm called FRACTAL-G. We compare the decrease in the number of remaining minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and sequential diagnosis while FRACTAL-P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the FRACTAL algorithms. We empirically evaluate the trade-offs of the three FRACTAL algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.

Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.

In the usual models of cooperative game theory, the outcome of a coalition formation process is either the grand coalition or a coalition structure that consists of disjoint coalitions. However, in many domains where coalitions are associated with tasks, an agent may be involved in executing more than one task, and thus may distribute his resources among several coalitions. To tackle such scenarios, we introduce a model for cooperative games with overlapping coalitionsor overlapping coalition formation (OCF) games. We then explore the issue of stability in this setting. In particular, we introduce a notion of the core, which generalizes the corresponding notion in the traditional (non-overlapping) scenario. Then, under some quite general conditions, we characterize the elements of the core, and show that any element of the core maximizes the social welfare. We also introduce a concept of balancedness for overlapping coalitional games, and use it to characterize coalition structures that can be extended to elements of the core. Finally, we generalize the notion of convexity to our setting, and show that under some natural assumptions convex games have a non-empty core. Moreover, we introduce two alternative notions of stability in OCF that allow a wider range of deviations, and explore the relationships among the corresponding definitions of the core, as well as the classic (non-overlapping) core and the Aubin core. We illustrate the general properties of the three cores, and also study them from a computational perspective, thus obtaining additional insights into their fundamental structure.