We survey the AI research carried out in Greece recently. A milestone for AI research in Greece came in 1988, when the Hellenic Artificial Intelligence Society (EETN) was founded as a nonprofit scientific organization devoted to organizing and promoting AI research in Greece and abroad. EETN is an affiliated society of the European Association for Artificial Intelligence (EurAI, formerly known as ECCAI). One of the many roles of EETN is the organization of conferences, workshops, summer schools, and other events, such as the Hellenic Conference on Artificial Intelligence (SETN). The first SETN was Science with a team well grounded in KR.

Usually, a CBR process is composed of four steps, namely: retrieve (selection of one or several case(s) from the base); reuse (adaptation of the retrieved case(s) to solve the new problem); revise (presentation of the newly formed case to application domain experts and, as appropriate, corrections to it); and retain (addition of the revised case to the case base, if this addition is judged useful). CBR is an active field of ICCBR is not only an important venue for presenting research that is application-and theory-driven, and it CBRrelated research. It is also an important relates to both machine learning and knowledge representation. Generous funding from NTNU, the Norwegian Each day of the conference began with an invited Research Council, and our other sponsors allowed talk. On the first day, Henri Prade presented an introduction the conference to cover all the meals for the attendees to analogical proportions and analogical reasoning during the conference.

While the amount of data stored in current information systems continuously grows, and the processes making use of such data become more and more complex, extracting knowledge and getting insights from these data, as well as governing both data and the associated processes, are still challenging tasks. The problem is complicated by the proliferation of data sources and services both within a single organization, and in cooperating environments. Effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by the information technology industry today. Indeed, it is not surprising that data scientists spend a comparatively large amount of time in the data preparation phase of a project, compared with the data minining and knowledge discovery phase. Whether you call it data wrangling, data munging, or data integration, it is estimated that 50%-80% of a data scientists time is spent on collecting and organizing data for analysis. If we consider that in any complex organization, data governance is also essential for tasks other than data analytics, we can conclude that the challenge of identifying, gathering, retaining, and providing access to all relevant data for the business at an acceptable cost, is huge.

The AAAI 2017 Fall Symposium Series was held Thursday through Saturday, November 9–11, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, DC. The titles of the six symposia were Artificial Intelligence for Human-Robot Interaction; Cognitive Assistance in Government and Public Sector Applications; Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; Human-Agent Groups: Studies, Algorithms and Challenges; Natural Communication for Human-Robot Collaboration; and A Standard Model of the Mind. The highlights of each symposium (except the Natural Communication for Human-Robot Collaboration symposium, whose organizers did not submit a report) are presented in this report.

'80s were equipped with state-of-the-art sensors and artificial The cleaning robots introduced during the 1990s were based on lessons learned. They came with less computational power and cheaper sensors. The first Roomba followed a very simple but effective principle: random traversal of the environment while bouncing back from walls, similar to a cue ball hitting a rail. Although this reactive behavior could not deliver guarantees on completeness, it offered a good compromise between functionality and price that satisfied people all over the world. More than 15 million of these robots have been sold.

Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.

The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwod Canyon in the Wasatch Range of the Rock Mountains near Salt Lake County, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynotes. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops.

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. KR will be held January 29-31, 2019 at 2018 will be held October 30 - No - the Hilton Hawaiian Village in Honolulu, vember 2, 2018, in Tempe, Arizona, Hawaii USA. The IAAI-19 Conference will be held January 29-31, 2019 at the Hilton Hawaiian Village in Honolulu, Hawaii USA. We invite all interested individuals to check out our Facebook site by searching for AAAI.

While artificial intelligence AAAI-19 will comprise a host of programs, well as strong outreach programs for including the Senior Member (AI) and human-computer interaction students, women, and sister conferences. Track, the Technical Demonstration (HCI) represent traditional They have absorbed all former Program, the Tutorial and Workshop mainstays of the conference, HCOMP special tracks into the main conference Programs, and several student programs, believes strongly in inviting, fostering, technical program, with provision for such as the Student Abstract and promoting broad, interdisciplinary distinguished oversight of reviews for and Poster Program and the Doctoral research. This field is particularly these areas.

Risk assessment is the process where we identify hazards, analyze or evaluate the risk associated with that hazard, and determine appropriate ways to eliminate or control the hazard. Risk assessment techniques have been widely applied in many area including quantitative financial engineering (Krokhmal et al. 2002), health and environment study (Zhang and Wang 2012; Van Asselt et al. 2013), transportation science (Liu et al. 2017), etc. Paltrinieri et al. (2014) point out that traditional risk assessment methods are often limited by static, onetime processes performed during the design phase of industrial processes. As such they often use older data or generic data on potential hazards and failure rates of equipment and processes and cannot be easily updated in order to take into account new information, giving a more complete view of the related risks. This failure to account for new information can lead to unrecognized hazards, or misunderstandings about the real probability of their occurrence under current management and safety precautions. With the rapid development of computational intelligence and corresponding decision support system, as well as the launch of "Big data" era, nowadays, new risk assessment technique should allow decision maker to update the assessment results by observing new information or data and realize quick response to dynamic environment. In this paper, we develop a satisficing measure based model to assess, compare and ranking random outcomes, and propose both online and offline data-driven computational frameworks.