Welcome to the Twenty-Fifth Annual Conference on the Innovative Applications of Artificial Intelligence (IAAI-13), the premier conference focusing on applied AI research ranging from exciting new potential applications to innovative deployments of AI technology. IAAI is colocated with AAAI and their paper presentations and invited talks are coordinated. This enables conference attendees to seamlessly move between conferences fostering interest in applied AI research while keeping track of the latest results of AI research. IAAI-13 will have 3 paper tracks: deployed applications, emerging applications and challenge problems. The deployed applications track focuses on fielded AI applications that distinguish themselves for their innovative use of AI technology.

To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node itself is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and parameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is comparable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models.

My work aims to create a scaffold for deployable intelligent systems using crowdsourcing. Current approaches in artificial intelligence (AI) typically focus on solving a narrow subset of problems in a given space - for example: automatic speech recognition as part of a conversational assistant, machine vision as part of a question answering service for blind people, or planning as part of a home assistive robot. This approach is necessary to scope the solution, but often results in a large number of systems that are rarely deployed in real-world setting, but instead operate in toy domains, or in situations where other parts of the problem are assumed to be solved. The framework I have developed aims to use the crowd to help in two ways: (i) make it possible to use human intelligence to power parts of a system that automated approaches cannot or do not yet handle, and (ii) provide a means of enabling more effective deployable systems by people to provide reliable training data on-demand. This summary begins with a brief review of prior work, then outlines a number of different system that I have developed to demonstrate the capabilities of this framework, and concludes with future work to be completed as part of my thesis.

This paper studies distributed cooperative multi-agent exploration methods in settings where the exploration is costly and the overall performance measure is determined by the minimum performance achieved by any of the individual agents. Such an exploration setting is applicable to various multi-agent systems, e.g., in Dynamic Spectrum Access exploration. The goal in such problems is to optimize the process as a whole, considering the tradeoffs between the quality of the solution obtained and the cost associated with the exploration and coordination between the agents. Through the analysis of the two extreme cases where coordination is completely free and when entirely disabled, we manage to extract the solution for the general case where coordination is taken to be costly, modeled as a fee that needs to be paid for each additional coordinated agent. The strategy structure for the general case is shown to be threshold-based, and the thresholds which are analytically derived in this paper can be calculated offline, resulting in a very low online computational load.

This special issue of the AI Magazine is devoted to some exemplar works of AI on mobile devices. It includes four works that range from mobile activity recognition and air quality detection to machine translation and image compression.

Learning by observation can be of key importance whenever agents sharing similar features want to learn from each other. This paper presents an agent architecture that enables software agents to learn by direct observation of the actions executed by expert agents while they are performing a task. This is possible because the proposed architecture displays information that is essential for observation, making it possible for software agents to observe each other. The agent architecture supports a learning process that covers all aspects of learning by observation, such as discovering and observing experts, learning from the observed data, applying the acquired knowledge and evaluating the agent's progress. The evaluation provides control over the decision to obtain new knowledge or apply the acquired knowledge to new problems. We combine two methods for learning from the observed information. The first one, the recall method, uses the sequence on which the actions were observed to solve new problems. The second one, the classification method, categorizes the information in the observed data and determines to which set of categories the new problems belong. Results show that agents are able to learn in conditions where common supervised learning algorithms fail, such as when agents do not know the results of their actions a priori or when not all the effects of the actions are visible. The results also show that our approach provides better results than other learning methods since it requires shorter learning periods.

In this report a computational study of ConceptNet 4 is performed using tools from the field of network analysis. Part I describes the process of extracting the data from the SQL database that is available online, as well as how the closure of the input among the assertions in the English language is computed. This part also performs a validation of the input as well as checks for the consistency of the entire database. Part II investigates the structural properties of ConceptNet 4. Different graphs are induced from the knowledge base by fixing different parameters. The degrees and the degree distributions are examined, the number and sizes of connected components, the transitivity and clustering coefficient, the cores, information related to shortest paths in the graphs, and cliques. Part III investigates non-overlapping, as well as overlapping communities that are found in ConceptNet 4. Finally, Part IV describes an investigation on rules.

Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the impact of climate change. However, success of machine learning-based approaches depends on a number of factors. While it can be safely said that no particular ML technique can be effective in all applications and success of a technique is predominantly dependent on the application or the type of the problem, it is useful to understand their behaviour to ensure informed choice of techniques. This paper presents a comprehensive review of machine learning-based bioclimatic model generation and analyses the factors influencing success of such models. Considering the wide use of statistical techniques, in our discussion we also include conventional statistical techniques used in bioclimatic modelling.

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton's method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization (MM) principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications.