I am a transitional research fellow in the Digital Creativity Hub at the University of York working on Artificial Intelligence (AI), data mining and machine learning for digital games and interactive media. I am also a member of the Artificial Intelligence and Games groups, in the Department of Computer Science. My research is focussed on using games to push boundaries in the capabilities of modern AI and making state of the art methods accessible to the industry to encourage a new generation of intelligent games.

The 2016 winners were as follows: Tom Dietterich, AAAI President, for AAAI 2017 Awards, please Manuela Veloso, AAAI Past President contact Carol Hamilton at hamilton@aaai.org.

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. The Tenth International AAAI Conference 15th International Conference on 18th International Conference on on Web and Social Media Principles of Knowledge Representation Enterprise Information Systems ICWSM-16 will be held May 17-20, and Reasoning (KR 2016) KR ICEIS 2016 will be held April 27-30, 2016 in Cologne, Germany. IEA/AIE-2016 will be York, New York USA. to Washington DC USA. AAAI-17 will be held in January-February in New Orleans, Louisiana USA.

While the Turing test is a well-known method for evaluating machine intelligence, it has a number of drawbacks that make it problematic as a rigorous and practical test for assessing progress in general-purpose AI. For example, the Turing test is deception based, subjectively evaluated, and narrowly focused on language use. We suggest that a test would benefit from including the following requirements: focus on rational behavior, test several dimensions of intelligence, automate as much as possible, score as objectively as possible, and allow incremental progress to be measured. In this article we propose a methodology for designing a test that consists of a series of events, analogous to the Olympic Decathlon, which complies with these requirements. The approach, which we call the I-athlon, is intended to ultimately enable the community to evaluate progress towards machine intelligence in a practical and repeatable way.

Autonomous agents are a huge trend in consumer, business, industry, and other domains. They're popping up in everything from physical devices -- such as Internet of things (IoT) endpoints and mobile handsets -- to cloud services such as virtual personal assistants and smart advisers. Autonomous IoT devices will allow us to multitask like never before. As we incorporate more of them into our lives, we can offload much of the drudgery we once needed to handle manually. We will let self-driving cars manage our commute, offload the more strenuous yardwork to our robotic household assistants, and depend on personal drones to keep an eye on the neighborhood.

Last year blockchain became one of the hottest topics in the world even much outside IT community. We saw a booming growth in the number of startups; large banks and even governments started investing in researching the technology. So definitely, there is much buzz about the topic, but seemingly it has nothing to do with machine learning and artificial intelligence. While articles and news on artificial intelligence as compared to blockchain and other cryptoworld topics are in no less degree blossoming these days, everybody keep silent on possible synergic implications in these truly breakthrough fields of computer science. At BICA Labs we do see a strong synergy between blockchain and AI -- a synergy that goes much beyond just an applied science.

This paper explores the problem of managing movements of aircraft along the surface of busy airports. Airport surface management is a complex logistics problem involving the coordination of humans and machines. The work described here arose from the idea that autonomous towing vehicles for taxiing aircraft could offer a solution to the 'capacity problem' for busy airports, the problem of getting more efficient use of existing surface area to meet increasing demand. Supporting autonomous surface operations requires continuous planning, scheduling and monitoring of operations, as well as systems for optimizing complex human-machine interaction. We identify a set of computational subproblems of the surface management problem that would benefit from recent advances in multi-agent planning and scheduling and probabilistic predictive modeling, and discuss preliminary work at integrating these components into a prototype of a surface management system.

In a complex adaptive system, diverse agents perform various actions without adherence to a predefined structure. The achievement of joint actions will be a result of continual interactions among them that shape a dynamic network. Agents may form an ad hoc organization based on dynamic network of interactions for the purpose of achieving a long term objective, which is called a Network Organization (NO). For the dominant influences of the network substrate in an NO, multiple effects of it have an impact on the NO behaviors and directions. We envisioned several dimensions of such effects to be synergy, social capital, externality, influence, etc. The focus of this paper is on measuring synergy as one of those possible network effects. Synergy describes different modalities of compatibility among agents when performing a set of coherent and correspondingly different actions. When agents are under no structural obligation to contribute, synergy is quantified through multiple forms of serendipitous agent chosen benevolence among them. The approach of this paper is to measure four types of benevolence and the pursuant synergies from them stemming from agent interactions. We exemplify this approach using a case study of a multiplayer online game.

As the rapid development of e-commerce, reputation model has been proposed to help customers make effective purchase decisions. However, most of reputation models focus only on the overall ratings of products without considering reviews which provided by customers. We believe that textual reviews provided by buyers can express their real opinions more honestly. As so, in this paper, based on word2vector model, we propose a Textual Reputation Model (TRM) to obtain useful information from reviews, and evaluate the trustworthiness of objective product. Experimental results on real data demonstrate the effectiveness of our approach in capturing reputation information from reviews.

The capability of collaboration is critical in the design of symbiotic cognitive systems. To obtain this functional capability, a cognitive system should possess evaluative and communicative processes. Emotions and their underlying processes provide such functions in social and collaborative environments. We investigate the mutual influence of affective and collaboration processes in a cognitive theory to support the interaction between humans and robots or virtual agents. We have developed new algorithms for these processes, as well as a new overall computational model for implementing collaborative robots and agents. We build primarily on the cognitive appraisal theory of emotions and the SharedPlans theory of collaboration to investigate the structure, fundamental processes and functions of emotions in a collaboration context.