David L. Waltz (1943-2012), was director, Center for Computational Learning Systems In 1973, Dave Waltz with Richard P. Gabriel in tow headed Dave Waltz delivers his AAAI Presidential Address at AAAI-98 in Madison, Wisconsin. While at Illinois, Dave produced system, paving the way for an engineering-style 11 Ph.D. students and many more MS students, approach to emergent AI techniques; and even mentored junior researchers and postdocs, attracted though their first attempts to create a multidisciplinary new AI faculty, and helped create the Beckman AI degree program failed, Dave was able in Institute for Advanced Science and Technology. In 1984, Marvin Minsky asked Dave to return to During the late 1970s and early 1980s, Waltz's Thinking Machines, Inc., an MIT spinoff in Cambridge group explored new ideas in natural language processing, -- with the temptation that the atmosphere cognitive science, qualitative reasoning, would be like the early days of the AI Lab all over and parallel computation in a collaborative environment again. At the same time he took a parttime including researchers in computer science, tenured position at Brandeis. Machines and Brandeis, Dave developed the ideas He chaired and brought the influential of massively parallel AI and, with Craig Stanfill, the Theoretical Issues in Natural Language Processing memory-based reasoning approach to case-based conference to Urbana in 1978.

At one of those conferences, I met John. Stanford moved toward a computer science department under the leadership of George Forsythe, John suggested to George, and then supported, the idea of hiring me into the founding faculty of the department. Since we were both Advanced Research Project Agency (ARPA) contract awardees, we quickly formed a close bond concerning ARPA-sponsored AI research and graduate student teaching. And the joint intelligence of both of us was quickly deployed in a very rapid and, in retrospect, brilliant decision to hire Les Earnest to be the executive officer of the new Stanford AI Lab that ARPA supported. John McCarthy's first breakthrough paper was his 1958 Teddington Symposium paper on programs with commonsense reasoning abilities.

Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers’ behavior, we found that improved learning models could be developed by adding information on drivers’ demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

In 2013, the Robotics information, please visit the IAAI-13 iaai13 icwsm13 track is particularly interested in website at www.aaai.org/iaai13.

We present a new formulation for attacking binary classification problems. Instead of relying on convex losses and regularisers such as in SVMs, logistic regression and boosting, or instead non-convex but continuous formulations such as those encountered in neural networks and deep belief networks, our framework entails a non-convex but \emph{discrete} formulation, where estimation amounts to finding a MAP configuration in a graphical model whose potential functions are low-dimensional discrete surrogates for the misclassification loss. We argue that such a discrete formulation can naturally account for a number of issues that are typically encountered in either the convex or the continuous non-convex paradigms, or both. By reducing the learning problem to a MAP inference problem, we can immediately translate the guarantees available for many inference settings to the learning problem itself. We empirically demonstrate in a number of experiments that this approach is promising in dealing with issues such as severe label noise, while still having global optimality guarantees. Due to the discrete nature of the formulation, it also allows for \emph{direct} regularisation through cardinality-based penalties, such as the $\ell_0$ pseudo-norm, thus providing the ability to perform feature selection and trade-off interpretability and predictability in a principled manner. We also outline a number of open problems arising from the formulation.

Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension. This is done using either a carefully designed projection or by a weight sharing technique. Via a novel unified analysis, we show that these two approaches deliver essentially equivalent bounds on a notion of regret generalizing shifting, adaptive, discounted, and other related regrets. Our analysis also captures and extends the generalized weight sharing technique of Bousquet and Warmuth, and can be refined in several ways, including improvements for small losses and adaptive tuning of parameters.

Given $\alpha,\epsilon$, we study the time complexity required to improperly learn a halfspace with misclassification error rate of at most $(1+\alpha)\,L^*_\gamma + \epsilon$, where $L^*_\gamma$ is the optimal $\gamma$-margin error rate. For $\alpha = 1/\gamma$, polynomial time and sample complexity is achievable using the hinge-loss. For $\alpha = 0$, \cite{ShalevShSr11} showed that $\poly(1/\gamma)$ time is impossible, while learning is possible in time $\exp(\tilde{O}(1/\gamma))$. An immediate question, which this paper tackles, is what is achievable if $\alpha \in (0,1/\gamma)$. We derive positive results interpolating between the polynomial time for $\alpha = 1/\gamma$ and the exponential time for $\alpha=0$. In particular, we show that there are cases in which $\alpha = o(1/\gamma)$ but the problem is still solvable in polynomial time. Our results naturally extend to the adversarial online learning model and to the PAC learning with malicious noise model.

This paper provides the first ---to the best of our knowledge--- analysis of online learning algorithms for multiclass problems when the {\em confusion} matrix is taken as a performance measure. The work builds upon recent and elegant results on noncommutative concentration inequalities, i.e. concentration inequalities that apply to matrices, and more precisely to matrix martingales. We do establish generalization bounds for online learning algorithm and show how the theoretical study motivate the proposition of a new confusion-friendly learning procedure. This learning algorithm, called \copa (for COnfusion Passive-Aggressive) is a passive-aggressive learning algorithm; it is shown that the update equations for \copa can be computed analytically, thus allowing the user from having to recours to any optimization package to implement it.

Imitation Learning has been shown to be successful in solving many challenging real-world problems. Some recent approaches give strong performance guarantees by training the policy iteratively. However, it is important to note that these guarantees depend on how well the policy we found can imitate the oracle on the training data. When there is a substantial difference between the oracle's ability and the learner's policy space, we may fail to find a policy that has low error on the training set. In such cases, we propose to use a coach that demonstrates easy-to-learn actions for the learner and gradually approaches the oracle. By a reduction of learning by demonstration to online learning, we prove that coaching can yield a lower regret bound than using the oracle. We apply our algorithm to a novel cost-sensitive dynamic feature selection problem, a hard decision problem that considers a user-specified accuracy-cost trade-off. Experimental results on UCI datasets show that our method outperforms state-of-the-art imitation learning methods in dynamic features selection and two static feature selection methods.

In this paper, we study latent factor models with the dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. The main benefit (novelty) of the model is that we can simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is devised to make the model feasible for large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by our model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.