The in AI program will be held in 36th Annual Conference of the Cognitive Conference Fete will be held at the conjunction with AAAI-14. The main Science Society, July 23-26, 2014; beautiful Le Theatre and Cabaret du goal of this program is to increase participation the Conference on Uncertainty in Artificial Capitole de Québec and will be open to of women and members of Intelligence, July 23-27, 2014; all attendees! Other special events are underrepresented groups in Artificial the Computational Neuroscience planned, including an update to the Intelligence by providing community Meeting, July 26-31, 2014; and Artificial 2013 Puzzle Hunt, so stay tuned for building and networking sessions as General Intelligence 2014, August more!

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This sy stem has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.

This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

Inappropriate prescribing of antimicrobials is a major clinical concern that affects as many as 50 percent of prescriptions. One of the difficulties of antimicrobial prescribing lies in the necessity to sequentially adjust the treatment of a patient as new clinical data become available. The lack of specialized healthcare resources and the overwhelming amount of information to process make manual surveillance unsustainable. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. However, the highly sensitive knowledge base used by the system leads to many false alerts. As a remedy, we are developing a machine learning algorithm that combines instance-based learning and rule induction techniques to discover new rules for detecting inappropriate prescriptions from previous false alerts. In this article, we describe the system, point to results and lessons learned so far and provide insight into the machine learning capability.

Future radiology practices assume that the radiology reports should be uniform, comprehensive, and easily managed. This means that reports must be readable to humans and machines alike. In order to improve reporting practices in breast imaging, we allow the radiologist to write structured reports with a special pen on paper with an invisible dot pattern. In this way, we provide a knowledge acquisition system for printed mammography patient forms for the combined work with printed and digital documents. In this domain, printed documents cannot be easily replaced by computer systems because they contain free-form sketches and textual annotations, and the acceptance of traditional PC reporting tools is rather low among the doctors. This is due to the fact that current electronic reporting systems significantly add to the amount of time it takes to complete the reports. We describe our real-time digital paper application and focus on the use case study of our deployed application. We think that our results motivate the design and implementation of intuitive pen-based user interfaces for the medical reporting process and similar knowledge work domains. Our system imposes only minimal overhead on traditional form-filling processes and provides for a direct, ontology-based structuring of the user input for semantic search and retrieval applications, as well as other applied artificial intelligence scenarios which involve manual form-based data acquisition.

We present an implementation of E-anti-unification as defined in Heinz (1995), where tree-grammar descriptions of equivalence classes of terms are used to compute generalizations modulo equational theories. We discuss several improvements, including an efficient implementation of variable-restricted E-anti-unification from Heinz (1995), and give some runtime figures about them. We present applications in various areas, including lemma generation in equational inductive proofs, intelligence tests, diverging Knuth-Bendix completion, strengthening of induction hypotheses, and theory formation about finite algebras.

Principal component analysis (PCA) is a standard tool in modern data analysis - in diverse fields from neuroscience to computer graphics - because it is a simple, nonparametric method for extracting relevant information from confusing data sets. With minimal effort PCA provides a roadmap for how to reduce a complex data set to a lower dimension to reveal the sometimes hidden, simplified structures that often underlie it. The goal of this tutorial is to provide both an intuitive feel for PCA, and a thorough discussion of this topic. We will begin with a simple example and provide an intuitive explanation of the goal of PCA. We will continue by adding mathematical rigor to place it within the framework of linear algebra to provide an explicit solution.

In this paper, we consider low rank matrix estimation using either matrix-version Dantzig Selector $\hat{A}_{\lambda}^d$ or matrix-version LASSO estimator $\hat{A}_{\lambda}^L$. We consider sub-Gaussian measurements, $i.e.$, the measurements $X_1,\ldots,X_n\in\mathbb{R}^{m\times m}$ have $i.i.d.$ sub-Gaussian entries. Suppose $\textrm{rank}(A_0)=r$. We proved that, when $n\geq Cm[r^2\vee r\log(m)\log(n)]$ for some $C>0$, both $\hat{A}_{\lambda}^d$ and $\hat{A}_{\lambda}^L$ can obtain optimal upper bounds(except some logarithmic terms) for estimation accuracy under spectral norm. By applying metric entropy of Grassmann manifolds, we construct (near) matching minimax lower bound for estimation accuracy under spectral norm. We also give upper bounds and matching minimax lower bound(except some logarithmic terms) for estimation accuracy under Schatten-q norm for every $1\leq q\leq\infty$. As a direct corollary, we show both upper bounds and minimax lower bounds of estimation accuracy under Ky-Fan-k norms for every $1\leq k\leq m$.

This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and supress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This "affect control principle" has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.

Bayesian networks are convenient graphical expressions for high dimensional probability distributions representing complex relationships between a large number of random variables. They have been employed extensively in areas such as bioinformatics, artificial intelligence, diagnosis, and risk management. The recovery of the structure of a network from data is of prime importance for the purposes of modeling, analysis, and prediction. Most recovery algorithms in the literature assume either discrete of continuous but Gaussian data. For general continuous data, discretization is usually employed but often destroys the very structure one is out to recover. Friedman and Goldszmidt suggest an approach based on the minimum description length principle that chooses a discretization which preserves the information in the original data set, however it is one which is difficult, if not impossible, to implement for even moderately sized networks. In this paper we provide an extremely efficient search strategy which allows one to use the Friedman and Goldszmidt discretization in practice.