AAAI-16 will also feature the following programs (preliminary list). The deadline for nominations is September Please make a note of the upcoming deadlines (subject to change): 30, 2015.

The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what intelligent systems need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim and, thus, there are great opportunities for participants in this competition.

However, what often matters for experience is the user's place A semantic model of user-centered places, the Platys ontology enables the mapping Research in context-aware computing (Schilit, Adams, of positions to places. In the model, places and and Want 1994) aims to enable computing systems that activities can be represented at different levels of acquire and maintain context data and use it to adapt granularity using subsumption hierarchies. It originated with Weiser's vision of to determine a user's place at any given time. Place ubiquitous computing (Weiser 1999) where human recognition has been addressed with standard activities are enhanced with devices that are all around machine-learning classifiers as well as a semisupervised but unnoticeable to the user and that provide services expectation-maximization algorithm. The that adapt to the circumstances in which they are used. Location is an 1994; Schilit et al. 1993) are early works in contextaware essential part of place and therefore place recognition computing and dealt with tracking a user's location relies on location sensing. Since frequent location and using it to provide better services or sharing it sensing by a mobile device depletes power, we have with others.

For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.

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.

Although Switzerland is a small country, it is home to many internationally renowned universities and scientific institutions. The research landscape in Switzerland is rich, and AI-related themes are investigated by many teams under diverse umbrellas. This column sheds some light on selected developments and trends on AI in Switzerland as perceived by members of the Special Interest group on Artificial Intelligence and Cognitive Science (SGAICO) organizational team, which has brought together researchers from Switzerland interested in AI and cognitive science for over 30 years.

AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.

An important research topic in artificial intelligence is automatic sensing and inferencing of contextual information, which is used to build computer models of the user’s activity. One approach to build such activity-aware systems is the notion of activity-based computing (ABC). ABC is a computing paradigm that has been applied in personal information management applications as well as in ubiquitous, multidevice, and interactive surface computing. ABC has emerged as a response to the traditional application- and file-centered computing paradigm, which is oblivious to a notion of a user’s activity context spanning heterogeneous devices, multiple applications, services, and information sources. In this article, we present ABC as an approach to contextualize information, and present our research into designing activity-centric computing technologies.

Although a number of initiatives provide personalized context-aware guidance for niche use-cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as, personal situation recognition and customizable rules for the context-sensitive management of personal devices and data sharing. As a proof-of-concept, we demonstrate how an innovative context-aware system has successfully adopted such an infrastructure.

Modern multicore computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009; Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show, that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.