Algorithm selection is of increasing practical relevance in a variety of applications. The interested reader is referred to a recent survey for more information (Kotthoff 2014). All submissions were required to provide the full source code, with instructions on how to run the system. In alphabetical order, the submitted systems were ASAP kNN, ASAP RF, autofolio, flexfolio-schedules, sunny, sunny-presolv, zilla, and zillafolio. The overall winner of the ICON challenge was zilla, based on the prominent SATzilla (Xu et al. 2008) system.

In many real-world systems, strategic agents' decisions can be understood as complex - i.e., consisting of multiple sub-decisions - and hence can give rise to an exponential number of pure strategies. Examples include network congestion games, simultaneous auctions, and security games. However, agents' sets of strategies are often structured, allowing them to be represented compactly. There currently exists no general modeling language that captures a wide range of commonly seen strategy structure and utility structure. We propose Resource Graph Games (RGGs), the first general compact representation for games with structured strategy spaces, which is able to represent a wide range of games studied in literature. We leverage recent results about multilinearity, a key property of games that allows us to represent the mixed strategies compactly, and, as a result, to compute various equilibrium concepts efficiently. While not all RGGs are multilinear, we provide a general method of converting RGGs to those that are multilinear, and identify subclasses of RGGs whose converted version allow efficient computation.

To achieve peak performance, it is often necessary to adjust the parameters of a given algorithm to the class of problem instances to be solved; this is known to be the case for popular solvers for a broad range of AI problems, including AI planning, propositional satisfiability (SAT) and answer set programming (ASP). To avoid tedious and often highly sub-optimal manual tuning of such parameters by means of ad-hoc methods, general-purpose algorithm configuration procedures can be used to automatically find performance-optimizing parameter settings. While impressive performance gains are often achieved in this manner, additional, potentially costly parameter importance analysis is required to gain insights into what parameter changes are most responsible for those improvements. Here, we show how the running time cost of ablation analysis, a well-known general-purpose approach for assessing parameter importance, can be reduced substantially by using regression models of algorithm performance constructed from data collected during the configuration process. In our experiments, we demonstrate speed-up factors between 33 and 14 727 for ablation analysis on various configuration scenarios from AI planning, SAT, ASP and mixed integer programming (MIP).

Computational mechanism analysis is a recent approach to economic analysis in which a mechanism design setting is analyzed entirely by a computer. For games with non-trivial numbers of players and actions, the approach is only feasible when these games can be encoded compactly, e.g., as Action-Graph Games. Such encoding is currently a manual process requiring expert knowledge; our aim is to simplify and automate it. Our contribution, the Positronic Economist is a software system having two parts: (1) a Python-based language for succinctly describing mechanisms; and (2) a system that takes such descriptions as input, automatically identifies computationally useful structure, and produces a compact Action-Graph Game.

Online health resources aimed at older adults can have a significant impact on patient-physician relationships and on health outcomes. High quality online resources that are delivered in an ethical, emotionally aligned way can increase trust and reduce negative health outcomes such as anxiety. In contrast, low quality or misaligned resources can lead to harmful consequences such as inappropriate use of health care services and poor health decision-making. This paper investigates mechanisms for ensuring both quality and alignment of online health resources and interventions. First, the recently proposed QUEST evaluation instrument is examined. QUEST assesses the quality of online health information along six validated dimensions (authorship, attribution, conflict of interest, currency, complementarity, tone). A decision tree classifier is learned that is able to predict one criteria of the QUEST tool, complementarity, with an F1-score of 0.9 on a manually annotated dataset of 50 articles giving advice about Alzheimer disease. A social-psychological theory of affective (emotional) alignment is then presented, and demonstrated to gauge older adults emotional interpretations of eight examples of health recommendation systems related to Alzheimer disease (online memory tests). The paper concludes with a synthesizing view and a vision for the future of this important societal challenge.

The ACM Conference on Intelligent User Interfaces (IUI) is the annual meeting of the intelligent user interface community and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces. ACM IUI is where the Human-Computer Interaction (HCI) community meets the Artificial Intelligence (AI) community. Here we summarize the latest trends in IUI based on our experience organizing the 20th ACM IUI Conference in Atlanta in 2015. At ACM IUI, we address the complex interactions between Figure 1: Take a Selfie with Hairware machine intelligence and human intelligence by leveraging solutions from machine learning, knowledge representation and new interaction technologies. Although submissions focusing paradigms have emerged. For example, at IUI 2015, conductive on only Artificial Intelligence (AI) or Human Computer hair extensions were used to send messages, record Interaction (HCI) will be considered, we give strong conversations and control cameras (Vega, Cunha, and Fuks preferences to submissions that discuss research from both 2015) (Figure 1).

Algorithms for NP-complete problems often have different strengths andweaknesses, and thus algorithm portfolios often outperform individualalgorithms. It is surprisingly difficult to quantify a component algorithm's contributionto such a portfolio. Reporting a component's standalone performance wronglyrewards near-clones while penalizing algorithms that have small but distinctareas of strength. Measuring a component's marginal contribution to an existingportfolio is better, but penalizes sets of strongly correlated algorithms,thereby obscuring situations in which it is essential to have at least onealgorithm from such a set. This paper argues for analyzing component algorithmcontributions via a measure drawn from coalitional game theory---the Shapleyvalue---and yields insight into a research community's progress over time. Weconclude with an application of the analysis we advocate to SAT competitions,yielding novel insights into the behaviour of algorithm portfolios, theircomponents, and the state of SAT solving technology.

We investigate the problem of repacking stations in the FCC's upcoming, multi-billion-dollar "incentive auction". Early efforts to solve this problem considered mixed-integer programming formulations, which we show are unable to reliably solve realistic, national-scale problem instances. We describe the result of a multi-year investigation of alternatives: a solver, SATFC, that has been adopted by the FCC for use in the incentive auction. SATFC is based on a SAT encoding paired with a wide range of techniques: constraint graph decomposition; novel caching mechanisms that allow for reuse of partial solutions from related, solved problems; algorithm configuration; algorithm portfolios; and the marriage of local-search and complete solver strategies. We show that our approach solves virtually all of a set of problems derived from auction simulations within the short time budget required in practice.

Compiling a Bayesian network into a secondary structure, such as a junction tree or arithmetic circuit allows for offline computations before observations arrive, and quick inference for the marginal of all variables. However, query-based algorithms, such as variable elimination and recursive conditioning, that compute the posterior marginal of few variables given some observations, allow pruning of irrelevant variables, which can reduce the size of the problem. Madsen and Jensen show how lazy evaluation of junction trees can allow both compilation and pruning. In this paper, we adapt the lazy evaluation to arithmetic circuits, allowing the best of both worlds: pruning due to observations and query variables as well as compilation while exploiting local structure and determinism.

This short paper describes experiences in the development of several smart power wheelchair platforms across three different sites. In the course of the project, we have re-used several of the components (both hardware and software) despite differences in the base platform of the robots. We describe the different platforms, and discuss some of the challenges and results of our work.