ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).

In answer-set programming (ASP), programs can be viewed as specifications of finite Herbrand structures. Other logics can be (and, in fact, were) used towards the same end and can be taken as the basis of declarative programming systems of similar functionality as ASP. We discuss here one such logic, the logic FO(ID), and its implementation IDP3. The choice is motivated by notable similarities between ASP and FO(ID), even if both approaches trace back to different origins

Domain model acquisition is the problem of learning the structure of a state-transition system from some input data, typically example transition sequences. Recent work has shown that it is possible to learn action costs of PDDL models, given the overall costs of individual plans. In this work we have explored the extension of these ideas to narrative planning where cost can represent a variety of features (e.g. tension or relationship strength) and where exact solutions don’t exist. Hence in this paper we generalise earlier results to show that when an exact solution does not exist, a best-fit costing can be generated. This approach is of particular interest in the context of plan-based narrative generation where the input cost functions are based on subjective input. In order to demonstrate the effectiveness of the approach, we have learnt models of narratives using subjective measures of narrative tension. These were obtained with narratives (presented as video in this case) that were encoded as action traces, and then scored for subjective narrative tension by viewers. This provided a collection of input plan traces for our domain model acquisition system to learn a best-fit model. Using this learnt model we demonstrate how it can be used to generate new narratives that fit different target levels of tension.

How to get started with Knowledge Representation? Any high signal, low noise books and papers to recommend for getting into KR? I read Brachman's Knowledge Representation and Reasoning, but kept to the slides for the most part. Only thing I read, though, so it's hard to contrast.

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reactiondiffusion process from data. Our solution relies on a nontrivial connection between stochastic reaction-diffusion processes and spatiotemporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatiotemporal stochastic systems, and a practical solution to a longstanding problem in computational modelling. Many complex behaviours in several disciplines originate from a common mechanism: the dynamics of locally interacting, spatially distributed agents. Examples arise at all spatial scales and in a wide range of scientific fields, from microscopic interactions of low-abundance molecules within cells, to ecological and epidemic phenomena at the continental scale. Frequently, stochasticity and spatial heterogeneity play a crucial role in determining the process dynamics and the emergence of collective behaviour [1]-[8]. Stochastic reaction-diffusion processes (SRDPs) constitute a convenient mathematical framework to model such systems. SRDPs were originally introduced in statistical physics [10, 11] to describe the collective behaviour of populations of point-wise agents performing Brownian diffusion in space and stochastically interacting with other, nearby agents according to predefined rules. The flexibility afforded by the local interaction rules has led to a wide application of SRDPs in many different scientific disciplines where complex spatiotemporal behaviours arise, from molecular biology [4, 9, 12], to ecology [13], to the social sciences [14]. Despite their popularity, SRDPs pose considerable challenges, as analytical computations are only possible for a handful of systems [8].

VIENTIANE – Southeast Asian nations are in unparalleled disarray over Beijing's saber-rattling in the South China Sea, analysts and insiders say, with the fractures set to deepen as staunch China ally Laos hosts top regional diplomats this weekend. U.S. Secretary of State John Kerry and Chinese Foreign Minister Wang Yi are among the delegates due to fly in from Sunday for two days of meetings in Vientiane, the capital of the communist nation. The South China Sea is set to cast a long shadow over the summit that is hosted by the 10-member Association of Southeast Asian Nations (ASEAN). Earlier this month a U.N.-backed tribunal found there was no legal basis for China's claims to most of the strategic and resource-rich seas -- a ruling rejected as "waste paper" by Beijing. ASEAN prides itself on consensus diplomacy but divisions have never been starker with Beijing blamed for driving a wedge between members. The Philippines brought the international arbitration case, while fellow ASEAN members Vietnam, Malaysia and Brunei also have competing claims to parts of the sea.

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities still classified as AI include advanced Chess and Go systems and self-driving cars. AI research is divided into subfields[4] that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[5] General intelligence is among the field's long-term goals.[6] Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it."[7] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[8] Attempts to create artificial intelligence has experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.[9]

Europe has a new speedcubing champion, Germany's Phillip Weyer. Weyer solved a Rubik's cube puzzle in an average time of 7.88 seconds in the Rubik's Cube European Championship 2016. The 2016 Championship held in Prague, Czech Republic, is the seventh Rubik's Cube European Championship. It had 18 categories, including the main category of the classic 3x3 cube, solving the cube blindfolded or only using the feet. Weyer told Reuters that it took years of hard work to get to the top.