Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complexity, which hinders their applications in real world high dimensional datasets, e.g gene expression datasets. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. The parallelised algorithms help speed up the procedure of experimenting big datasets and reduce the memory used when running the algorithms. The package is not only suitable for super-computers or clusters, but also convenient for researchers using personal computers with multi core CPUs. Our experiment results on real world datasets show that using the parallelised algorithms it is now practical to explore causal relationships in high dimensional datasets with thousands of variables in a single multicore computer. ParallelPC is available in CRAN repository at https://cran.rproject.org/web/packages/ParallelPC/index.html.

Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones. We relatively rapidly add new features by skipping over the costly optimisation of parameters. We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels. These prove effective as high-order features, and are fast to compute. Several schemes for selecting high-order features by composition or search of a small subclass are compared. Additionally we present a simple modification of the maximum likelihood as a texture modelling-specific objective function which aims to improve generalisation by local windowing of statistics. The proposed method was experimentally evaluated by learning high-order MGRF models for a broad selection of complex textures and then performing texture synthesis, and succeeded on much of the continuum from stochastic through irregularly structured to near-regular textures. Learning interaction structure is very beneficial for textures with large-scale structure, although those with complex irregular structure still provide difficulties. The texture models were also quantitatively evaluated on two tasks and found to be competitive with other works: grading of synthesised textures by a panel of observers; and comparison against several recent MGRF models by evaluation on a constrained inpainting task.

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. BIOSTEC 2016 will be held 21-23 February, 2016, in Third AAAI Conference on Human 15th International Conference on Rome, Italy Computation and Crowdsourcing. HCOMP 2015 will be held November and Reasoning (KR 2016) 8-11 in San Diego, California. ICAART 2016 will be held 24-26 February, AAAI Fall Symposium.

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus.

The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditions in learning theory under which fast learning is possible. We show that most of these conditions are special cases of a single, unifying condition, that comes in two forms: the central condition for 'proper' learning algorithms that always output a hypothesis in the given model, and stochastic mixability for online algorithms that may make predictions outside of the model. We show that under surprisingly weak assumptions both conditions are, in a certain sense, equivalent. The central condition has a re-interpretation in terms of convexity of a set of pseudoprobabilities, linking it to density estimation under misspecification. For bounded losses, we show how the central condition enables a direct proof of fast rates and we prove its equivalence to the Bernstein condition, itself a generalization of the Tsybakov margin condition, both of which have played a central role in obtaining fast rates in statistical learning. Yet, while the Bernstein condition is two-sided, the central condition is one-sided, making it more suitable to deal with unbounded losses. In its stochastic mixability form, our condition generalizes both a stochastic exp-concavity condition identified by Juditsky, Rigollet and Tsybakov and Vovk's notion of mixability. Our unifying conditions thus provide a substantial step towards a characterization of fast rates in statistical learning, similar to how classical mixability characterizes constant regret in the sequential prediction with expert advice setting.

Planning as satisfiability is known as an efficient approach to deal with many types of planning problems. However, this approach has not been competitive with the state-space based methods in temporal planning. This paper describes ITSAT as an efficient SAT-based (satisfiability based) temporal planner capable of temporally expressive planning. The novelty of ITSAT lies in the way it handles temporal constraints of given problems without getting involved in the difficulties of introducing continuous variables into the corresponding satisfiability problems. We also show how, as in SAT-based classical planning, carefully devised preprocessing and encoding schemata can considerably improve the efficiency of SAT-based temporal planning. We present two preprocessing methods for mutex relation extraction and action compression. We also show that the separation of causal and temporal reasoning enables us to employ compact encodings that are based on the concept of parallel execution semantics. Although such encodings have been shown to be quite effective in classical planning, ITSAT is the first temporal planner utilizing this type of encoding. Our empirical results show that not only does ITSAT outperform the state-of-the-art temporally expressive planners, it is also competitive with the fast temporal planners that cannot handle required concurrency.

The last 10 years have seen the development of novel architectures and technologies for domainfocused, task-specific systems that know many things, such as who (identities, profile, history) they are with (social context) and in what role (responsibility, security, privacy); when and where (event, time, place); why (goals, shared or personal); how are they doing it (methods, applications); and using what resources (device, services, access, and ownership). Smart spaces and devices will increasingly use such contextual knowledge to help users move seamlessly between devices and applications, without having to explicitly carry, transfer, and exchange activity context. Such systems will qualitatively shift our lives both at work and play and significantly change our interactions both with our physical and virtual worlds. This dream of seamlessly interacting with our virtual environment has a long history as can be seen in Apple Inc.'s Knowledge Navigator 1987 concept video. However, the combination of dramatic progress in low-power mobile computing devices and sensors, with advances in artificial intelligence and human-computer interaction (HCI) in the last decade, have provided the kind of platforms and algorithms that are enabling context-aware virtual personal assistants that plan activities and recognize intent. This has lead to an increase in work designed to bring these ideas into real world application and address the final technical hurdles that will make such systems a reality.

Cost-optimal planning (COP) uses action costs and asks for a minimum-cost plan. It is sometimes assumed that there is no harm in using actions with zero cost or rational cost. Classical complexity analysis does not contradict this assumption; planning is PSPACE-complete regardless of whether action costs are positive or non-negative, integer or rational. We thus apply parameterised complexity analysis to shed more light on this issue. Our main results are the following. COP is [W2]-complete for positive integer costs, i.e. it is no harder than finding a minimum-length plan, but it is paraNP-hard if the costs are non-negative integers or positive rationals. This is a very strong indication that the latter cases are substantially harder. Net-benefit planning (NBP) additionally assigns goal utilities and asks for a plan with maximum difference between its utility and its cost. NBP is paraNP-hard even when action costs and utilities are positive integers, suggesting that it is harder than COP. In addition, we also analyse a large number of subclasses, using both the PUBS restrictions and restricting the number of preconditions and effects.

Personalized technologies aim to enhance user experience by taking into account users' interests, preferences, and other relevant information. Persuasive technologies aim to modify user attitudes, intentions, or behavior through computer-human dialogue and social influence. While both personalized and persuasive technologies influence user interaction and behavior, we posit that this influence could be significantly increased if the two are combined to create personalized and persuasive systems. For example, the persuasive power of a one-size-fits-all persuasive intervention could be enhanced by considering the user being influenced and their susceptibility to the persuasion being offered. Likewise, personalized technologies could cash in on increased successes, in terms of user satisfaction, revenue, and user experience, if their services used persuasive techniques.

This paper considers the kinds of AI systems we want involved in art and art practice. We explore this relationship from three perspectives: as artists interested in expanding and developing our own creative practice; as AI researchers interested in building new AI systems that contribute to the understanding and development of art and art practice; and as audience members interested in experiencing art. We examine the nature of both art practice and experiencing art to ask how AI can contribute. To do so, we review the history of work in intelligent agents which broadly speaking sits in two camps: autonomous agents (systems that can exhibit intelligent behaviour independently) in one, and multi-agent systems (systems which interact with other systems in communities of agents) in the other. In this context we consider the nature of the relationship between AI and Art and introduce two opposing concepts: that of “Heroic AI”, to describe the situation where the software takes on the role of the lone creative hero and “Collaborative AI” where the system supports, challenges and provokes the creative activity of humans. We then set out what we believe are the main challenges for AI research in understanding its potential relationship to art and art practice.