The development of domain-independent planners This modular approach also supports the use of reformulation within the AI Planning community is leading to and configuration techniques which can automatically "off the shelf" technology that can be used in a reformulate, re-represent or tune the domain model and/or wide range of applications. Moreover, it allows a problem description in order to increase the efficiency of modular approach - in which planners and domain a planner and increase the scope of problems solved. The knowledge are modules of larger software applications idea is to make these techniques to some degree independent - that facilitates substitutions or improvements of domain and planner (that is, applicable to a range of individual modules without changing the of domains and planning engine technologies), and use them rest of the system. This approach also supports the to form a wrapper around a planner, improving its overall use of reformulation and configuration techniques, performance for the domain to which it is applied. Types which transform how a model is represented in order of reformulation include macro-learning [Botea et al., 2005; to improve the efficiency of plan generation. Newton et al., 2007], action schema splitting [Areces et al., In this paper, we investigate how the performance 2014] and entanglements [Chrpa and McCluskey, 2012]: here of planners is affected by domain model configuration.

In dynamic settings where data is exposed by streams, knowledge discovery aims at learning associations of data across streams. In the semantic Web, streams expose their meaning through evolutive versions of ontologies. Such settings pose challenges of scalability for discovering (a posteriori) knowledge. In our work, the semantics, identifying knowledge similarity and rarity in streams, together with incremental, approximate maintenance, control scalability while preserving accuracy of streams associations (as semantic rules) discovery.

Automatic algorithm configurators are important practical tools for improving program performance measures, such as solution time or prediction accuracy. Local search approaches in particular have proven very effective for tuning algorithms. In sequential local search, the use of predictive models has proven beneficial for obtaining good tuning results. We study the use of non-parametric models in the context of population-based algorithm configurators. We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring as well as sexual selection of parents. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically.

We present a computational framework for chord invention based on a cognitive-theoretic perspective on conceptual blending. The framework builds on algebraic specifications, and solves two musicological problems. It automatically finds transitions between chord progressions of different keys or idioms, and it substitutes chords in a chord progression by other chords of a similar function, as a means to create novel variations. The approach is demonstrated with several examples where jazz cadences are invented by blending chords in cadences from earlier idioms, and where novel chord progressions are generated by inventing transition chords.

Shubik's (all-pay) dollar auction is a simple yet powerful auction model that aims to shed light on the motives and dynamics of conflict escalation. Common intuition and experimental results suggest that the dollar auction is a trap, inducing conflict by its very design. However, O'Neill proved the surprising fact that, contrary to the experimental results and the intuition, the dollar auction has an immediate solution in pure strategies, i.e., theoretically it should not lead to conflict escalation. In this paper, inspired by the recent literature on spiteful bidders, we ask whether the escalation in the dollar auction can be induced by meanness. Our results confirm this conjecture in various scenarios.

We study a general class of multiagent optimization problems, together with a compact representation language of utilities based on weighted propositional formulas. We seek solutions maximizing utilitarian social welfare as well as fair solutions maximizing the utility of the least happy agent. We show that many problems can be expressed in this setting, such as fair division of indivisible goods, some multiwinner elections, or multifacility location. We focus on the complexity of finding optimal solutions, and we identify the tractability boarder between polynomial and NP-hard settings, along several parameters: the syntax of formulas, the allowed weights, as well as the number of agents, propositional symbols, and formulas per agent.

It allows a constraint to play each one. Each machine, after being used, returns a reward solver to exploit various levels of propagation during from a distribution specific to that machine. The goal is search, and in many cases it shows better performance to maximize the sum of rewards obtained through a sequence than static/predefined. The crucial point of plays [Gittins, 1989]. is to make adaptive constraint propagation automatic, We use a MAB model to select the right level of propagation so that no expert knowledge or parameter (also called level of consistency) to enforce at each node specification is required. In this work, we propose during the exploration of the search tree. We specify a simple a simple learning technique, based on multiarmed reward function and the upper confidence bound (UCB) to estimate bandits, that allows to automatically select the best arm, namely the best consistency to apply.

The Scaffolded Sound Beehive is an immersive multi-media installation which provides viewers an artistic visual and audio experience of activities in a beehive. Data were recorded in urban beehives and processed using sophisticated pattern recognition, AI technologies, and sonification and computer graphics software. The installation includes an experiment in using Deep Learning to interpret the activities in the hive based on sound and microclimate recording.

In the contemporary autonomous systems the role of complex interactions such as (possibly relaxed) dialogues is increasing significantly. In this paper we provide a paraconsistent and paracomplete implementation of inquiry dialogue under realistic assumptions regarding availability and quality of information. Various strategies for dealing with unsure and inconsistent information are analyzed. The corresponding dialogue outcomes are further evaluated against the (paraconsistent and paracomplete) distributed beliefs of the group. A specific 4-valued logic underpins the presented framework. Thanks to the qualities of the implementation tool: a rule-based query language 4QL, our solution is both expressive and tractable.

A popular technique for the analysis of web query languages relies on the translation of queries into logical formulas. These formulas are then solved for satisfiability using an off-the-shelf satisfiability solver. A critical aspect in this approach is the size of the obtained logical formula, since it constitutes a factor that affects the combined complexity of the global approach. We present logical combinators whose benefit is to provide an exponential gain in succinctness in terms of the size of the logical representation. This opens the way for solving a wide range of problems such as satisfiability and containment for expressive query languages in exponential-time, even though their direct formulation into the underlying logic results in an exponential blowup of the formula size, yielding an incorrectly presumed two-exponential time complexity. We illustrate this from a practical point of view on a few examples such as numerical occurrence constraints and tree frontier properties which are concrete problems found with semi-structured data.