This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. The Tenth International AAAI Conference 15th International Conference on 18th International Conference on on Web and Social Media Principles of Knowledge Representation Enterprise Information Systems ICWSM-16 will be held May 17-20, and Reasoning (KR 2016) KR ICEIS 2016 will be held April 27-30, 2016 in Cologne, Germany. IEA/AIE-2016 will be York, New York USA. to Washington DC USA. AAAI-17 will be held in January-February in New Orleans, Louisiana USA.

We review the First International Competition on Computational Models of Argumentation (ICMMA’15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

In this article I argue that achieving human-level AI is equivalent to learning how to create sufficiently smart software social organisms. This implies that no single test will be sufficient to measure progress. Instead, evaluations should be organized around showing increasing abilities to participate in our culture, as apprentices. This provides multiple dimensions within which progress can be measured, including how well different interaction modalities can be used, what range of domains can be tackled, what human-normed levels of knowledge they are able to acquire, as well as others. I begin by motivating the idea of software social organisms, drawing on ideas from other areas of cognitive science, and provide an analysis of the substrate capabilities that are needed in social organisms in terms closer to what is needed for computational modeling. Finally, the implications for evaluation are discussed.

If the artificial intelligence research community is to have a challenge problem as an incentive for research, as many have called for, it behooves us to learn the principles of past successful inducement prize competitions. Those principles argue against the Turing test proper as an appropriate task, despite its appropriateness as a criterion (perhaps the only one) for attributing intelligence to a machine.

This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.

Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence. The much ballyhooed 2015 Turing test winner Eugene Goostman, for instance, pretends to be a thirteen-year-old foreigner and proceeds mainly by ducking questions and returning canned one-liners; it cannot see, it cannot think, and it is certainly a long way from genuine artificial general intelligence.

Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current IRL approaches assume that if the transition model is unknown, additional samples from the system's dynamics are accessible, or the observed behavior provides enough samples of the system's dynamics to solve the inverse problem accurately. These assumptions are often not satisfied. To overcome this, we present a gradient-based IRL approach that simultaneously estimates the system's dynamics. By solving the combined optimization problem, our approach takes into account the bias of the demonstrations, which stems from the generating policy. The evaluation on a synthetic MDP and a transfer learning task shows improvements regarding the sample efficiency as well as the accuracy of the estimated reward functions and transition models.

A BSTRACT Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function a neuron should perform. However, this leads to an extensive increase in the computational complexity of the training procedure. We present a novel, parameterizable transfer function based on the mathematical concept of non-integer functional iteration that allows the operation each neuron performs to be smoothly and, most importantly, differentiablely adjusted between addition and multiplication. This allows the decision between addition and multiplication to be integrated into the standard backpropagation training procedure. The value of such a product unit is given byy i σ ( j x W ij j).

In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase in the number of classes comes along with increased ambiguity between the class labels, raising the question if top-1 error is the right performance measure. In this paper, we provide an extensive comparison and evaluation of established multiclass methods comparing their top-k performance both from a practical as well as from a theoretical perspective. Moreover, we introduce novel top-k loss functions as modifications of the softmax and the multiclass SVM losses and provide efficient optimization schemes for them. In the experiments, we compare on various datasets all of the proposed and established methods for top-k error optimization. An interesting insight of this paper is that the softmax loss yields competitive top-k performance for all k simultaneously. For a specific top-k error, our new top-k losses lead typically to further improvements while being faster to train than the softmax.

We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions. Our algorithm draws heavily from a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as a recent approach to variance reduction for stochastic gradient descent from Johnson and Zhang (2013). We demonstrate experimentally that our algorithm performs well on large-scale convex and non-convex optimization problems, exhibiting linear convergence and rapidly solving the optimization problems to high levels of precision. Furthermore, we show that our algorithm performs well for a wide-range of step sizes, often differing by several orders of magnitude.