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Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available when algorithms are selected and run. We investigate how this online data can be used to improve the selection model over time. This is especially relevant when insufficient training instances were used, but potentially improves the performance of algorithm selection in all cases. We formally define the online algorithm selection problem and model it as a contextual multi-armed bandit problem, propose a methodology for solving it, and empirically demonstrate performance improvements. We also show that our online algorithm selection method can be used when no training data whatsoever is available, a setting where offline algorithm selection cannot be used. Our experiments indicate that a simple greedy approach achieves the best performance.

A lot of technological advances depend on next-generation materials, such as graphene, which enables better electronics, to name but one example. Manufacturing such materials is often difficult, in particular, producing graphene at scale is an open problem. We apply state-of-the-art machine learning to optimize the production of laser-induced graphene, an established manufacturing method that has shown great promise. We demonstrate improvements over previous results in terms of the quality of the produced graphene from a variety of different precursor materials. We use Bayesian model-based optimization to quickly improve outcomes based on little initial data and show the robustness of our approach to different experimental conditions, tackling a small-data problem in contrast to the more common big-data applications of machine learning. We analyze the learned surrogate models with respect to the quality of their predictions and learned relationships that may be of interest to domain experts and improve our understanding of the processes governing laser-induced graphene production.

Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks. The process of designing HPO algorithms, however, is still an unsystematic and manual process: Limitations of prior work are identified and the improvements proposed are -- even though guided by expert knowledge -- still somewhat arbitrary. This rarely allows for gaining a holistic understanding of which algorithmic components are driving performance, and carries the risk of overlooking good algorithmic design choices. We present a principled approach to automated benchmark-driven algorithm design applied to multifidelity HPO (MF-HPO): First, we formalize a rich space of MF-HPO candidates that includes, but is not limited to common HPO algorithms, and then present a configurable framework covering this space. To find the best candidate automatically and systematically, we follow a programming-by-optimization approach and search over the space of algorithm candidates via Bayesian optimization. We challenge whether the found design choices are necessary or could be replaced by more naive and simpler ones by performing an ablation analysis. We observe that using a relatively simple configuration, in some ways simpler than established methods, performs very well as long as some critical configuration parameters have the right value., Comment: * Equal Contributions

The control of the physical, chemical, and electronic properties of laser-induced graphene (LIG) is crucial in the fabrication of flexible electronic devices. However, the optimization of LIG production is time-consuming and costly. Here, we demonstrate state-of-the-art automated parameter tuning techniques using Bayesian optimization to advance rapid single-step laser patterning and structuring capabilities with a view to fabricate graphene-based electronic devices. In particular, a large search space of parameters for LIG explored efficiently. As a result, high-quality LIG patterns exhibiting high Raman G/D ratios at least a factor of four larger than those found in the literature were achieved within 50 optimization iterations in which the laser power, irradiation time, pressure and type of gas were optimized. Human-interpretable conclusions may be derived from our machine learning model to aid our understanding of the underlying mechanism for substrate-dependent LIG growth, e.g. high-quality graphene patterns are obtained at low and high gas pressures for quartz and polyimide, respectively. Our Bayesian optimization search method allows for an efficient experimental design that is independent of the experience and skills of individual researchers, while reducing experimental time and cost and accelerating materials research.

Meta-algorithmics, the field of leveraging machine learning to use algorithms more efficiently, has achieved impressive performance improvements in many areas of AI. It treats the algorithms to improve on as black boxes – nothing is known about their inner workings. This allows meta-algorithmic techniques to be deployed in many applications, but leaves potential performance improvements untapped by ignoring information that the algorithms could provide. In this paper, we open the black box without sacrificing the universal applicability of meta-algorithmic techniques by automatically analyzing the source code of the algorithms under consideration and show how to use it to improve algorithm selection performance. We demonstrate improvements of up to 82% on the standard ASlib benchmark library.

Variations of unique and tailored composite microstructures have been observed in nature and have served as templates for the development of new synthetic materials. Microstructures are studied in fish scales for their penetration resistance, in spider webs for energy absorption, and in seashells and bone for their strength and toughness. However, it has proven difficult to reproduce the properties found in natural materials due to the interaction between the intricate structures at different length scales. Rather than attempting to replicate these materials (biomimetics), the focus of this work is to use a bio-inspired pattern generation algorithm to search for new topologies that outperform traditional composite structures due to their naturelike design. The bio-inspired pattern generation algorithm employed in this research is known as the Gray-Scott model. This model was selected due to its unique ability to manufacture patterns that propagate with time, allowing the reinforcement volume fraction of the composite structure to be controlled. The model is capable of producing Turing patterns, propagating wave fronts, homogeneous oscillations, and chaos. Traditionally, Turing models have been primarily studied for their applications in morphogenesis and pattern development. However, this research extends the application of the Gray-Scott model by investigating the patterns as physical load bearing structures. A methodology was developed by which the patterns can be converted to structures, analyzed for a desired mechanical property, and optimized via Bayesian machine learning algorithms that yield an improvement of the average quality of structures produced by nearly a factor of 10.

The optimization of decentralized energy systems is an important practical problem that can be modeled using stochastic programs and solved via their large-scale, deterministic-equivalent formulations. Unfortunately, using this approach, even when leveraging a high degree of parallelism on large high-performance computing systems, finding close-to-optimal solutions still requires substantial computational effort. In this work, we present a procedure to reduce this computational effort substantially, using a state-of-the-art automated algorithm configuration method. We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storage units, modeled as a two-stage stochastic mixed-integer linear program. We demonstrate that the computing time and costs can be substantially reduced by up to 50% by use of our procedure. Our methodology can be applied to other, similarly-modeled energy systems.

Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time and energy consumption. Performance models allow to understand and predict the effects of such configuration options on system behavior, but are costly to build because of large configuration spaces. Performance models from one environment cannot be transferred directly to another; usually models are rebuilt from scratch for different environments, for example different hardware. Recently, transfer learning methods have been applied to reuse knowledge from performance models trained in one environment in another. In this paper, we perform an empirical study to understand the effectiveness of different transfer learning strategies for building performance models of DNN systems. Our results show that transferring information on the most influential configuration options and their interactions is an effective way of reducing the cost to build performance models in new environments., Comment: 2 pages, 2 figures, USENIX Conference on Operational Machine Learning, 2019

WEKA is a widely used, open-source machine learning platform. Due to its intuitive interface, it is particularly popular with novice users. However, such users often find it hard to identify the best approach for their particular dataset among the many available. We describe the new version of Auto-WEKA, a system designed to help such users by automatically searching through the joint space of WEKA's learning algorithms and their respective hyperparameter settings to maximize performance, using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated with WEKA, making it just as accessible to end users as any other learning algorithm.

A machine learning technique was used to predict static, failure envelopes of unidirectional composite laminas under combined normal (longitudinal or transverse) and shear loading at different biaxial ratios. An artificial neural network was chosen for this purpose due to their superior computational efficiency and ability to handle nonlinear relationships between inputs and outputs. Training and test data for the neural network were taken from the experimental composite failure data for glass- and carbonfiber reinforced epoxies provided by the world-wide failure exercise (WWFE) program. A quadratic, stress interactive Tsai-Wu failure theory was calibrated based on the reported strength values, as well as optimized from the experimental failure data points. The prediction made by the neural network was compared against the Tsai-Wu failure criterion predictions and it was observed that the trained neural network provides a better representation of the experimental data.

Assessing the progress made in AI and contributions to the state of the art is of major concern to the community. Recently, Frechette et al. [2016] advocated performing such analysis via the Shapley value, a concept from coalitional game theory. In this paper, we argue that while this general idea is sound, it unfairly penalizes older algorithms that advanced the state of the art when introduced, but were then outperformed by modern counterparts. Driven by this observation, we introduce the temporal Shapley value, a measure that addresses this problem while maintaining the desirable properties of the (classical) Shapley value. We use the tempo- ral Shapley value to analyze the progress made in (i) the different versions of the Quicksort algorithm; (ii) the annual SAT competitions 2007–2014; (iii) an annual competition of Constraint Programming, namely the MiniZinc challenge 2014–2016. Our analysis reveals novel insights into the development made in these important areas of research over time.

Graphene-derived materials are of significant interest in the manufacturing of functional devices due to their exceptional physical and chemical properties. The challenge in standardizing the synthesis of pristine graphene layers makes patterning of carbon materials a more favorable fabrication method. However, the graphene patterning process is often impeded due to high temperatures or toxic chemicals. Thus, it is paramount to find a straightforward and reproducible processing technique in a large scale. Laser reduction of graphene oxide and polymers have gained attention since it is a one-step chemical-free process to fabricate graphene circuits on-the-fly down to micrometer scales. The selective and locally confined laser-reduced circuits can be written on almost any solid or flexible substrate, showing promising potential in the manufacturing of advanced devices such as supercapacitors, energy storage devices and biosensors. In our work, we investigate strain effects on the electrical properties of graphene circuits on flexible substrates. We also utilize in-situ Raman and optical analysis with a view to automate the fabrication process in a closed-feedback loop. Furthermore, we present results of state-of-the-art model-based optimization in materials science. In particular, we use surrogate models for automated parameter tuning to optimize the manufacturing of laser-reduced graphene circuits. Our results are reproducible even with sparse training data and we discuss possibilities of human-less advanced manufacturing.

We present a large-scale investigation of the variability of run times on physical and virtualised hardware. The aim of our investigation is to establish whether cloud infrastructures are suitable for running computational experiments in which the results rely on reported run times. Our application is the use of the Minion constraint solver as an example of an Artificial Intelligence experiment. We include two major providers of public cloud infrastructure, Amazon and Rackspace, as well as a private Eucalyptus cloud. Although there are many studies in the literature that investigate the performance of cloud environments, the problem of whether this performance is consistent and run time measurements are reliable has been largely ignored. Our comprehensive experiments and detailed analysis of the results show that there is no intrinsic disadvantage of virtualised hardware over physical hardware and that in general cloud environments are suitable for running computational experiments. Our meticulous investi...

In this document we describe the health show case for the ICON project. This corresponds to Task 6.2 in WP 6 of the Description of Work for the project. The description provides a high-level abstraction, detailed description of the interfaces between modules, and a description of sample data.

In this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.

There exists a proliferation of different approaches to using portfolios and algorithm selection to make solving combinatorial search and optimisation problems more efficient, as surveyed in the previous chapter. In this chapter, we take a look at a detailed case study that leverages transformations between problem representations to make portfolios more effective, followed by extensions to the state of the art that make algorithm selection more robust in practice.

Not applicable: edited book.

The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This chapter contrasts and compares different methods for solving the problem as well as ways of using these solutions.

Machine learning is an established method of selecting algorithms to solve hard search problems. Despite this, to date no systematic comparison and evaluation of the different techniques has been performed and the performance of existing systems has not been critically compared with other approaches. We compare the performance of a large number of different machine learning techniques from different machine learning methodologies on five data sets of hard algorithm selection problems from the literature. In addition to well-established approaches, for the first time we also apply statistical relational learning to this problem. We demonstrate that there is significant scope for improvement both compared with existing systems and in general. To guide practitioners, we close by giving clear recommendations as to which machine learning techniques are likely to achieve good performance in the context of algorithm selection problems. In particular, we show that linear regression and alternating decision trees have a very high probability of achieving better performance than always selecting the single best algorithm.

We report the first evaluation of Constraint Satisfaction as a computational framework for solving closest string problems. We show that careful consideration of symbol occurrences can provide search heuristics that provide several orders of magnitude speedup at and above the optimal distance. We also report the first analysis and evaluation – using any technique – of the computational difficulties involved in the identification of all closest strings for a given input set. We describe algorithms for web-scale distributed solution of closest string problems, both purely based on AI backtrack search and also hybrid numeric-AI methods.

We founded recomputation.org about 18 months ago as we write. The site is intended to serve as a repository for computational experiments, embodied in virtual machines so that they can be recomputed at will by other researchers. We reflect in this paper on those aspects of recomputation.org that have worked well, those that have worked less well, and to what extent our views have changed on reproducibility in computational science.

We present a robust and generic framework for web-scale distributed e-Science Artificial Intelligence search. Our validation approach is to distribute constraint satisfaction problems that require perfect accuracy to 10, 12 and 15 digits. By checking solutions obtained using the framework against known results, we can ensure that no errors, duplications nor omissions are introduced. Unlike other approaches, we do not require dedicated machines, homogeneous infrastructure or the ability to communicate between nodes. We give special consideration to the robustness of the framework, minimising the loss of effort even after a total loss of infrastructure, and allowing easy verification of every step of the distribution process. The unique challenges our framework tackles are related to the combinatorial explosion of the space that contains the possible solutions, and the robustness of long-running computations. Not only is the time required to finish the computations unknown, but also the resource requirements may change during the course of the computation. We demonstrate the applicability of our framework by using it to solve challenging problems using two separate large-scale distribution paradigms. The results show that our approach scales to e-Science computations of a size that would have been impossible to tackle just a decade ago.

A key strength of agent-based modelling is the ability to explore the upward-causation of micro-dynamics on the macro-level behaviour of a system. However, in policy contexts, it is also important to be able to represent downward-causation from the macro and meso-levels to the micro, and to represent decision-making at the macro level (i.e., by governments) in a sensible way. Though we cannot model political processes easily, we can try to optimise decisions given certain stated goals (e.g., minimum cost, or maximum impact). Optimisation offers one potential method to model macro-level decisions in this way. This paper presents the implementation of an integration of optimisation with agent-based modelling for the example of an auction scenario of government support for the installation of photovoltaic solar panels by households. Auction type scenarios of this kind, in which large groups of individuals or organisations make bids for subsidies or contracts from government, are common in many policy domains. Proceedings 28th European Conference on Modelling and Simulation © ECMS Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani (Editors).

In recent years, portfolio approaches to solving SAT problems and CSPs have become increasingly common. There are also a number of different encodings for representing CSPs as SAT instances. In this paper, we leverage advances in both SAT and CSP solving to present a novel hierarchical portfolio-based approach to CSP solving, which we call Proteus, that does not rely purely on CSP solvers. Instead, it may decide that it is best to encode a CSP problem instance into SAT, selecting an appropriate encoding and a corresponding SAT solver. Our experimental evaluation used an instance of Proteus that involved four CSP solvers, three SAT encodings, and six SAT solvers, evaluated on the most challenging problem instances from the CSP solver competitions, involving global and intensional constraints. We show that significant performance improvements can be achieved by Proteus obtained by exploiting alternative view-points and solvers for combinatorial problem-solving.

The Algorithm Selection Problem [8] is to select the most appropriate algorithm for solving a particular problem. It is especially relevant in the context of algorithm portfolios [2, 3], where a single solver is replaced with a set of solvers and a mechanism for selecting a subset to use on a particular problem. A common way of doing algorithm selection is to train a machine learning model and predict the best algorithm from a portfolio to solve a particular problem.

One approach to automated constraint modelling is to generate, and then select from, a set of candidate models. This method is used by the automated modelling system Conjure. To select a preferred model or set of models for a problem class from the candidates Conjure produces, we use a set of training instances drawn from the target class. It is important that the training instances are discriminating. If all models solve a given instance in a trivial amount of time, or if no models solve it in the time available, then the instance is not useful for model selection. This paper addresses the task of generating small sets of discriminating training instances automatically. The instance space is determined by the parameters of the associated problem class. We develop a number of methods of finding parameter configurations that give discriminating training instances, some of them leveraging existing parameter-tuning techniques. Our experimental results confirm the success of our approach in reducing a large set of input models to a small set that we can expect to perform well for the given problem class.

The Algorithm Selection Problem is to select the most appropriate way for solving a problem given a choice of different ways. Some of the most prominent and successful applications come from Artificial Intelligence and in particular combinatorial search problems. Machine Learning has established itself as the de facto way of tackling the Algorithm Selection Problem. Yet even after a decade of intensive research, there are no established guidelines as to what kind of Machine Learning to use and how. This dissertation presents an overview of the field of Algorithm Selection and associated research and highlights the fundamental questions left open and problems facing practitioners. In a series of case studies, it underlines the difficulty of doing Algorithm Selection in practice and tackles issues related to this. The case studies apply Algorithm Selection techniques to new problem domains and show how to achieve significant performance improvements. Lazy learning in constraint solving and the implementation of the all different constraint are the areas in which we improve on the performance of current state of the art systems. The case studies furthermore provide empirical evidence for the effectiveness of using the misclassification penalty as an input to Machine Learning. After having established the difficulty, we present an effective technique for reducing it. Machine Learning ensembles are a way of reducing the background knowledge and experimentation required from the researcher while increasing the robustness of the system. Ensembles do not only decrease the difficulty, but can also increase the performance of Algorithm Selection systems. They are used to much the same ends in Machine Learning itself. We finally tackle one of the great remaining challenges of Algorithm Selection – which Machine Learning technique to use in practice. Through a large-scale empirical evaluation Constraints (2015) 20:481–482 DOI 10.1007/s10601-015-9214-x * Lars Kotthoff larsko@cs.ubc.ca 1 BETA Lab, Department of Computer Science, University of British Columbia, Vancouver, BC, Canada on diverse data taken from Algorithm Selection applications in the literature, we establish recommendations for Machine Learning algorithms that are likely to perform well in Algorithm Selection for combinatorial search problems. The recommendations are based on strong empirical evidence and additional statistical simulations. The research presented in this dissertation significantly reduces the knowledge threshold for researchers who want to perform Algorithm Selection in practice. It makes major contributions to the field of Algorithm Selection by investigating fundamental issues that have been largely ignored by the research community so far. School: University of St Andrews Supervisors: Ian Miguel Ian Gent Graduated: Wednesday, June 20, 2012 Link to full text: http://www.a4cp.org/sites/default/files/lars_kotthoff_-_on_algorithm_ selection_with_an_application_to_combinatorial_search_problems.pdf 482 Constraints (2015) 20:481–482

When solving a constraint satisfaction problem using a systematic backtracking method the branching scheme normally selects a variable to which a value is assigned. In this paper we refer to such strategies as eager branching schemes. These contrast with the alternative class of novel branchings considered in this paper whereby having selected a variable we proceed by removing values from its domain. In this paper we study such lazy branching schemes in depth. We define three lazy branchings based on k-way, binary and split branching. We show how each can be incorporated into MAC, and define a novel value ordering heuristic that is suitable in this setting. Our results show that lazy branching can significantly out-perform traditional branching schemes across a variety of problem classes. While, in general, neither lazy nor eager branching dominates the other, our results clearly show that choosing the correct branching scheme for a given problem instances can significantly reduce search effort. Therefore, we implemented a variety of branching portfolios for choosing amongst all of the branching strategies studied in this paper. The results demonstrate that a good branching scheme can be automatically selected for a given problem instances and that including lazy branching schemes in the portfolio significantly reduces runtime.

Constraint modelling is widely recognised as a key bottleneck in applying constraint solving to a problem of interest. The CONJURE automated constraint modelling system addresses this problem by automatically refining constraint models from problem specifications written in the Essence language. ESSENCE provides familiar mathematical concepts like sets, functions and relations nested to any depth. To date, Conjure has been able to produce a set of alternative model kernels (i.e. without advanced features such as symmetry breaking or implied constraints) for a given specification. The first contribution of this paper is a method by which CONJURE can break symmetry in a model as it is introduced by the modelling process. This works at the problem class level, rather than just individual instances, and does not require an expensive detection step after the model has been formulated. This allows CONJURE to produce a higher quality set of models. A further limitation of CONJURE has been the lack of a mechanism to select among the models it produces. The second contribution of this paper is to present two such mechanisms, allowing effective models to be chosen automatically.

The number of finite semigroups increases rapidly with the number of elements. Since existing counting formulae do not give the complete number of semigroups of given order up to equivalence, the remainder can only be found by careful search. We describe the use of mathematical results combined with distributed Constraint Satisfaction to show that the number of non-equivalent semigroups of order 10 is 12,418,001,077,381,302,684. This solves a previously open problem in Mathematics, and has directly led to improvements in Constraint Satisfaction technology.

The automatic tuning of the parameters of algorithms and automatic selection of algorithms has received a lot of attention recently. One possible approach is the use of machine learning techniques to learn classifiers which, given the characteristics of a particular problem, make a decision as to which algorithm or what parameters to use. Little research has been done into which machine learning algorithms are suitable and the impact of picking the "right" over the "wrong" technique. This paper investigates the differences in performance of several techniques on different data sets. It furthermore provides evidence that by using a meta-technique which combines several machine learning algorithms, we can avoid the problem of having to pick the "best" one and still achieve good performance.

Machine learning is an established method of selecting algorithms to solve hard search problems. Despite this, to date no systematic comparison and evaluation of the different techniques has been performed and the performance of existing systems has not been critically compared to other approaches. We compare machine learning techniques for algorithm selection on real-world data sets of hard search problems. In addition to well-established approaches, for the first time we also apply statistical relational learning to this problem. We demonstrate that most machine learning techniques and existing systems perform less well than one might expect. To guide practitioners, we close by giving clear recommendations as to which machine learning techniques are likely to perform well based on our experiments.

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.

Algorithm selection is of increasing practical relevance in a variety of applications. Many approaches have been proposed in the literature, but their evaluations are often not comparable, making it hard to judge which approaches work best. The ICON Challenge on Algorithm Selection objectively evaluated many prominent approaches from the literature, making them directly comparable for the first time. The results show that there is still room for improvement, even for the very best approaches.

In this paper a reinforcement learning methodology for automatic online algorithm selection is introduced and empirically tested. It is applicable to automatic algorithm selection methods that predict the performance of each available algorithm and then pick the best one. The experiments confirm the usefulness of the methodology: using online data results in better performance. As in many online learning settings an exploration vs. exploitation trade-off, synonymously learning vs. earning trade-off, is incurred. Empirically investigating the quality of classic solution strategies for handling this trade-off in the automatic online algorithm selection setting is the secondary goal of this paper. The automatic online algorithm selection problem can be modelled as a contextual multi-armed bandit problem. Two classic strategies for solving this problem are tested in the context of automatic online algorithm selection: epsilon-greedy and lower confidence bound. The experiments show that a simple purely exploitative greedy strategy outperforms strategies explicitly performing exploration. ispartof: pages:93-101 ispartof: ITAT 2016 Proceedings vol:1649 pages:93-101 ispartof: Information technologies -- Applications and Theory location:Tatranské Matliare date:15 Sep - 18 Sep 2016 status: published

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).