One of the key issues for transportation companies is to produce an optimal plan for the work of crew members. Crew planning consists of a sequence of phases, the first two corresponding to planning duties (sequences of trips to be done by crew members from their home base to their home base) and planning rosters (sequences of duties and rest days to be followed by crew members during a certain number of weeks). Both duty and roster planning are subject to a large number of constraints. Duty planning is constrained by intra-duty constraints and roster planning by inter-duty constraints. Since inter-duty constraints relate how duties can be combined into a roster, it is desirable that some of these constraints be transposed into the duty planning phase, as additional constraints, to guarantee that the duties produced in the first phase are "rosterable'' in the second phase. Both Artificial Intelligence (AI) and Operations Research (OR) have addressed duty planning, but for very large scale problems, OR has been far more successful due to its global vision of the problem. This paper discusses the use of AI local search to improve an OR-based duty planning optimizer that uses additional constraints.

We present a novel approach to bidirectional breadth-first IDA* (BFIDA*) and demonstrate its effectiveness in the domain of peg solitaire, a simple puzzle. Our approach improves upon unidirectional BFIDA* by usually avoiding the last iteration of search entirely, greatly speeding up search. In addition, we provide a number of improvements specific to peg solitaire. We have improved duplicate-detection in the context of BFIDA*. We have strengthened the heuristic used in the previous state-of-the-art solver. Finally, we use bidirectional search frontiers to provide a stronger technique for pruning unsolvable states. The combination of these approaches allows us to improve over the previous state-of-the-art, often by a two-orders-of-magnitude reduction in search time.

NASA and the international community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically heli- copters and civil tilt rotors. However, there is significant con- cern over the impact of noise on the communities surrounding the transportation facilities. One way to address the rotorcraft noise problem is by exploiting powerful search techniques coming from artificial intelligence coupled with simulation and field tests to design low-noise flight profiles which can be tested in simulation or through field tests. This paper in- vestigates the use of simulation based on predictive physical models to facilitate the search for low-noise trajectories using local search combined with a robust noise simulator.

The Partner Units Problem (PUP) constitutes a challenging real-world configuration problem with diverse application domains such as railway safety, security monitoring, electrical engineering, or distributed systems.Although using the latest problem-solving methods including Constraint Programming, SAT Solving,Integer Programming, and Answer Set Programming, current methods fail to generate solutions for mid-sized real-world problems in acceptable time. This paper presents the QuickPup algorithm based on backtrack search combined with smart variable orderings and restarts. QuickPup outperforms the available methods by orders of magnitude and thus makes it possible toautomatically solve problems which couldn’t be solved without human expertise before. Furthermore, the runtimes of QuickPup are typically below one second for real-world problem instances.

Dots-And-Boxes is a well-known and widely-played combinatorial game. While the rules of play are very simple, the state space for even very small games is extremely large, and finding the outcome under optimal play is correspondingly hard. In this paper we introduce a Dots-And-Boxes solver which is significantly faster than the current state-of-the-art: over an order-of-magnitude faster on several large problems. Our approach uses Alpha-Beta search and applies a number of techniques---both problem-specific and general---that reduce the search space to a manageable size. Using these techniques, we have determined for the first time that Dots-And-Boxes on a board of 4 x 5 boxes is a tie given optimal play; this is the largest game solved to date.

In the multi agent path finding problem (MAPF) paths shouldbe found for several agents, each with a different start andgoal position such that agents do not collide. Previous optimalsolvers applied global A*-based searches. We presenta new search algorithm called Conflict Based Search (CBS).CBS is a two-level algorithm. At the high level, a search isperformed on a tree based on conflicts between agents. At thelow level, a search is performed only for a single agent at atime. In many cases this reformulation enables CBS to examinefewer states than A* while still maintaining optimality.We analyze CBS and show its benefits and drawbacks. Experimentalresults on various problems shows a speedup ofup to a full order of magnitude over previous approaches.

Planning with action concurrency under time and resources constraints and uncertainty is a challenging problem. Current approaches which rely on Markov Decision Processes and a discrete model for time and resources are limited by a blow-up of the search state-space. This paper presents a planner which is based on a classical forward search for solving this kind a problems. A continuous model is used for time and resources. The uncertainty on time is represented by continuous random variables which are organized in a dynamically generated Bayesian network. Two versions of the ActuPlan planner are presented. As a first step, ActuPlan_nc performs a forward-search in an augmented state-space to generate epsilon-optimal nonconditional plans which are robust to uncertainty (threshold on the probability of success). ActuPlan_nc is then adapted to generate a set of nonconditional plans which are characterized by different trade-offs between their probability of success and their expected cost. ActuPlan, the second version, builds a conditional plan with a lower expected cost by merging previously generated nonconditional plans. The branches are built by conditioning on the time. Empirical experimentation on standard benchmarks demonstrates the effectiveness of the approach.

A* is the algorithm of finding the shortest path between two nodes in a graph. When the searching problem is constituted of a set of linked graphs, A* searches solution like if it is face of one graph formed by linked graphs. While researchers have developed solutions to reduce the execution time of A* in multiple cases by multiples techniques, we develop a new algorithm: DEC-A* which is a decentralized version of A* composing a solution through a collection of graph. A* uses a distance-plus-cost heuristic function to determine the order in which the search visits nodes in the tree. Our algorithm DEC-A* extends the evaluation of the distance-plus-cost heuristic to be the sum of two functions : local distance, which evaluates the cost to reach the nearest neighbor node s to the goal, and global distance which evaluates the cost from s to the goal through other graphs. DEC-A* reduces the time of finding the shortest path and reduces the complexity, while ensuring the privacy of graphs.

In an Artificial Intelligence course, a basic concept is Constraint Satisfaction (CS), which is acknowledged as a hard domain for teachers to teach and student to understand. In this paper, we present a game-based learning approach to assist students in learning CS algorithms, such as arc consistency and search algorithms, for problem solving in an easy, interactive and motivating way. Preliminary valuation has showed promising results.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of three AI assignments from the 2012 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.