While many computer science programs offer courses on research methods, such classes typically tend to be aimed at graduate students. In this paper, we propose a novel means for introducing undergraduate students to research experiences in computer science — via an introductory Artificial Intelligence (A.I.) course. Students explore the content areas typically covered in an upper-level A.I. course (heuristic search, constraint satisfaction, game-playing etc.), while also learning about the mechanics of how empirical research is conducted in this field.

Profound knowledge about Artificial Intelligence (AI) will become increasingly important for careers in science and engineering. Therefore an innovative educational project teaching fundamental concepts of AI at high school level will be presented in this paper. We developed an AI-course covering major topics (problem solving, search, planning, graphs, datastructures, automata, agent systems, machine learning) which comprises both theoretical and hands-on components. A pilot project was conducted and empirically evaluated. Results of the evaluation show that the participating pupils have become familiar with those concepts and the various topics addressed. Results and lessons learned from this project form the basis for further projects in different schools which intend to integrate AI in future secondary science education.

Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity, i.e., the degree of variation of life. Conservation planning seeks to reach this goal by means of deliberate actions, aimed at the protection (or restoration) of biodiversity features. In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. In particular, we propose a constrained optimisation formulation of the habitat restoration planning (HRP) problem, capturing aspects such as population dynamics and uncertainty. We show that the HRP is NP-hard, and develop a constraint programming (CP) model and a large neighbourhood search (LNS) procedure to generate activity plans under budgeting constraints.

Unsupervised domain adaptation (UDA) deals with the task that labeled training and unlabeled test data collected from source and target domains, respectively. In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. That is, we do not assume the label numbers across domains to be the same, and we also allow the data in each domain to be collected from multiple datasets/sub-domains. To solve the above task of imbalanced domain adaptation, we propose a novel algorithm of Domain-constraint Transfer Coding (DcTC). Our DcTC is able to exploit latent subdomains within and across data domains, and learns a common feature space for joint adaptation and classification purposes. Without assuming balanced cross-domain data as most existing UDA approaches do, we show that our method performs favorably against state-of-the-art methods on multiple cross-domain visual classification tasks.

Branching heuristics based on counting solutions in constraints have been quite good at guiding search  to solve constraint satisfaction problems. But do they perform as well for constraint optimization problems? We propose an adaptation of counting-based search for optimization, show how to modify solution density computation for some of the most frequently-occurring constraints, and empirically evaluate its performance on several benchmark problems.

Restarts are an important technique to make search more robust. This paper is concerned with how to maintain and propagate nogoods recorded from restarts efficiently. It builds on reduced nld-nogoods introduced for restarts and increasing nogoods introduced for symmetry breaking. The paper shows that reduced nld-nogoods extracted from a single restart are in fact increasing, which can thus benefit from the efficient propagation algorithm of the incNGs global constraint. We present a lighter weight filtering algorithm for incNGs in the context of restart-based search using dynamic event sets (dynamic subscriptions). We show formally that the lightweight version enforces GAC on each nogood while reducing the number of subscribed decisions. The paper also introduces an efficient approximation to nogood minimization such that all shortened reduced nld-nogoods from the same restart are also increasing and can be propagated with the new filtering algorithm. Experimental results confirm that our lightweight filtering algorithm and approximated nogood minimization successfully trade a slight loss in pruning for considerably better efficiency, and hence compare favorably against existing state-of-the-art techniques.

The pruning power of partial symmetry breaking depends on the given subset of symmetries to break as well as the interactions among symmetry breaking constraints. In the context of Partial Symmetry Breaking During Search (ParSBDS), the search order determines the set of symmetry breaking constraints to add and thus also makes an impact on node and solution pruning. In this paper, we give the first formal characterization of the pruning behavior of ParSBDS and its improved variants. Introducing the notion of Dominance-Completeness (DC-ness), we show that ParSBDS and variants eliminate the symmetry group of the given subset of symmetries if the resultant search tree is DC, and give an example scenario. Unfortunately, building a DC tree is not always possible. We propose two search heuristics with the aim of having more nodes dominated and thus also pruned during search. Extensive experimentation demonstrates how the proposed heuristics and their combination can drastically reduce the solution set size, search space and runtime when compared against the state-of-the-art static and dynamic symmetry breaking methods.

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.

Many problems, and in particular routing problems, require to find one or many circuits in a weighted graph. The weights often express the distance or the travel time between vertices. We propose in this paper various filtering algorithms for the weighted circuit constraint which maintain a circuit in a weighted graph. The filtering algorithms are typical cost based filtering algorithms relying on relaxations of the Traveling Salesman Problem. We investigate three bounds and show that they are incomparable. In particular we design a filtering algorithm based on a lower bound introduced in 1981 by Christophides et al.. This bound can provide stronger filtering than the classical Held and Karp’s approach when additional information, such as the possible positions of the clients in the tour, is available. This is particularly suited for problems with side constraints such as time windows.

The Steiner Tree Problem is a well know NP-complete problem that is well studied and for which fast algorithms are already available. Nonetheless, in the real world the Steiner Tree Problem is almost always accompanied by side constraints which means these approaches cannot be applied. For many problems with side constraints, only approximation algorithms are known. We introduce here a propagator for the tree constraint with explanations, as well as lower bounding techniques and a novel constraint programming approach for the Steiner Tree Problem and two of its variants. We find our propagators with explanations are highly advantageous when it comes to solving variants of this problem.