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.

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.

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.

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.

This article presents new algorithms for inferring users' activities in a class We also show that visualizing students' plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions. Such tools can plan visualization outperformed those students who provide support for teachers and education were presented with the list of activities for all of researchers in analyzing and assessing students' use these measures. These contributions demonstrate the benefit of Students' interactions with ELEs are complex, as applying novel plan-recognition technologies toward we illustrate using concepts from an ELE for teaching intelligent analysis of students' interactions in openended the basics of chemistry. Students can engage in and flexible software. Such technologies can exploratory activities involving trial and error, such potentially support teachers in their understanding as searching for the right pair of chemicals to combine of student behavior as well as students in their problem in order to achieve a desired reaction. They can solving and lead to advances in automatic recognition repeat activities indefinitely in pursuit of a goal or in other exploratory domains.

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.

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.

ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010. The main conference track featured 19 research paper presentations, 16 posters, and two invited speakers. The papers and posters reflected the state of the art of case-based reasoning, dealing both with open problems at the core of casebased reasoning (especially in similarity assessment, case adaptation, and case-based maintenance), as well as trending applications of CBR. Minor, Goethe University, Germany, and Emmanuel The first invited speaker, Tony Veale from University Nauer, LORIA, France.

We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets.

We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks.