This includes the physical world, where qualitative representations have a long track record of providing human-level reasoning and performance (Forbus 2014), but also in social reasoning (for example, degrees of blame [Tomai and Forbus 2007]). Qualitative representations carve up continuous phenomena into symbolic descriptions that serve as a bridge between perception and cognition, facilitate everyday reasoning and communication, and help ground expert reasoning. We close with some lessons (Forbus, Klenk, and Hinrichs 2009) is on higher-order learned and open problems. In Newell's (1990) timescale proposed that analogy involves the construction of decomposition of cognitive phenomena, conceptual mappings between two structured, relational representations. Thus to the other, based on the correspondences), and a we approximate subsystems whose operations occur score indicating the overall quality of the match. For which one is trying to reason about, and hence inferences example, in Companions constraint checking and are made from base to target by default.

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. ICWSM-18 will be held June 25-28 at Stanford University adjacent to Palo 10th International Conference on Alto, California USA. ICAART 2018 will be held January, 16-will be held February 2-7 at the Hilton Sixth AAAI Conference on Human 18, 2018, in Funchal, Madeira, Portuga. The APA Technology, Mind, and Louisiana USA.

Pablo Gervás's talk, How Creative Can Reuse Be? pointed up CBR as a favored The main conference program comprised 31 contributions between presentations and posters from 144 authors on technical and applied CBR papers. The origins of the Conference on Case-Based Reasoning The accepted papers were of very high quality, and date from the first European workshop on provided many new insights across a wide range of CBR (EWCBR) held in Kaiserslautern, Germany, in CBR issues. Topics in recent CBR research included in 1993. Since then many European and international the presentations and discussions at ICCBR 2016 conferences on CBR have been held in different parts included novel approaches to similarity and retrieval; of the world. The European conference on CBR advances in adaptation strategies; case generation; representation and knowledge discovery; CBR as a (ECCBR) and the International Conference on CBR cognitive approach to big data; AI with large-scale (ICCBR) were held in alternating years.

The first IEA/AIE conference was organized in 1988 in Tullahoma, Tennessee. Since that time, the conference has been held internationally in many countries including Germany, Scotland, Australia, Spain, Egypt, Hungary, England, Italy, France, Japan, Poland, China, Taiwan, Netherlands, and South Korea. The conference has always been sponsored by ISAI and all conferences have been held in cooperation with AAAI. AI, and Intelligent Systems in Health Care and The focus of the 2017 conference was on research mHealth for Health Outcomes advances in new and innovative intelligent systems' IEA/AIE-2017 also organized two workshops: ASP methodologies and their applications in solving reallife, Technologies for Querying Large-Scale Multiple-complex problems. In many worldwide applications, Source Heterogeneous Web Information, cochaired there is a real need to develop intelligent systems by Odile Papini, Salem Benferhat, Laurent Garcia, that deal with complex, open, and dynamic and Marie-Laure Mugnier; and Computer Animation information systems.

Bostrom’s Superintelligence (SI) is a wide-ranging essay (2016) that has raised important questions about the future of intelligent machines and the possible malign developments they may undergo. But, and perhaps surprisingly, it is not about technical developments in artificial intelligence (AI) nor a philosophical analysis of the concept of SI. There is little of either of these in it, which is largely an extended and stimulating essay on economics, decision theory and other forms of social science, all held together by the unsubstantiated hypothesis of “superintelligence” that belongs more to science fiction than AI. AI may well in some future produce undesirable social effects — the Internet itself could already be such a development — but there is as yet no reason to think they could be on the massive and end-of-civilization scale Bostrom so confidently predicts.

The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on Integrated Cognition, but is extended here through a synthesis across three existing cognitive architectures: ACT-R, Sigma, and Soar. The resulting standard model spans key aspects of structure and processing, memory and content, learning, and perception and motor, and highlights loci of architectural agreement as well as disagreement with the consensus while identifying potential areas of remaining incompleteness. The hope is that this work will provide an important step toward engaging the broader community in further development of the standard model of the mind.

The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimately reflect the needs of users yet be sufficiently sensitive to guide algorithm development. Feedback from critical care clinicians who use automated event detection software in clinical applications has been overwhelmingly emphatic that a low false alarm rate, typically measured in units of the number of errors per 24 hours, is the single most important criterion for user acceptance. Though using a single metric is not often as insightful as examining performance over a range of operating conditions, there is a need for a single scalar figure of merit. In this paper, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance. We demonstrate these metrics on a seizure detection task based on the TUH EEG Corpus. We show that two promising metrics are a measure based on a concept borrowed from the spoken term detection literature, Actual Term-Weighted Value, and a new metric, Time-Aligned Event Scoring (TAES), that accounts for the temporal alignment of the hypothesis to the reference annotation. We also demonstrate that state of the art technology based on deep learning, though impressive in its performance, still needs significant improvement before it will meet very strict user acceptance guidelines.

Learning and memory are intertwined in our brain and their relationship is at the core of several recent neural network models. In particular, the Attention-Gated MEmory Tagging model (AuGMEnT) is a reinforcement learning network with an emphasis on biological plausibility of memory dynamics and learning. We find that the AuGMEnT network does not solve some hierarchical tasks, where higher-level stimuli have to be maintained over a long time, while lower-level stimuli need to be remembered and forgotten over a shorter timescale. To overcome this limitation, we introduce hybrid AuGMEnT, with leaky or short-timescale and non-leaky or long-timescale units in memory, that allow to exchange lower-level information while maintaining higher-level one, thus solving both hierarchical and distractor tasks.

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.

In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact object is a black hole or a neutron star. To assist this classification, we develop a Bayesian statistical model that makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a 3-dimensional coordinate system derived from X-ray spectral data. The first coordinate of this data is the ratio of counts in mid to low energy band (color 1), the second coordinate is the ratio of counts in high to low energy band (color 2), and the third coordinate is the sum of counts in all three bands. We use this model to estimate the probabilities that an X-ray binary system contains a black hole, non-pulsing neutron star, or pulsing neutron star. In particular, we utilize a latent variable model in which the latent variables follow a Gaussian process prior distribution, and hence we are able to induce the spatial correlation we believe exists between systems of the same type. The utility of this approach is evidenced by the accurate prediction of system types using Rossi X-ray Timing Explorer All Sky Monitor data, but it is not flawless. In particular, non-pulsing neutron systems containing "bursters" that are close to the boundary demarcating systems containing black holes tend to be classified as black hole systems. As a byproduct of our analyses, we provide the astronomer with public R code that can be used to predict the compact object type of X-ray binaries given training data.