Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system's component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements.

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. This short paper discusses the history and implications of learning g-costs, both alone and in conjunction with learning h-costs as an introduction to the new f-LRTA* algorithm which learns both.

In a number of graph search-based planning problems, the value of the cost function that is being minimized also affects the set of possible actions at some or all the states in the graph. In such planning problems, the cost function typically becomes one of the state variables thereby increasing the dimensionality of the planning problem, and consequently the size of the graph that represents the problem. In this paper, we show how to avoid this increase in the dimensionality for weighted search (with bounded suboptimality) whenever the availability of the actions is monotonically non-increasing with the increase in the cost function.

Social networks play an increasingly important role in today's society. Special characteristics of these networks make them challenging domains for the search community. In particular, social networks of users can be viewed as search graphs of nodes, where the cost of obtaining information about a node can be very high. This paper addresses the search problem of identifying the degree of separation between two users. New search techniques are introduced to provide optimal or near-optimal solutions. The experiments are performed using Twitter, and they show an improvement of several orders of magnitude over greedy approaches. Our optimal algorithm finds an average degree of separation of 3.43 between two random Twitter users, requiring an average of only 67 requests for information over the Internet to Twitter. A near-optimal solution of length 3.88 can be found by making an average of 13.3 requests.

Rapidly-exploring random trees (RRTs) are data structures and search algorithms designed to be used in continuous path planning problems. They are one of the most successful state-of-the-art techniques as they offer a great degree of flexibility and reliability. However, their use in other search domains has not been thoroughly analyzed. In this work we propose the use of RRTs as a search algorithm for automated planning. We analyze the advantages that this approach has over previously used search algorithms and the challenges of adapting RRTs for implicit and discrete search spaces.

Tree search is a common technique for solving constraint satisfaction and combinatorial optimization problems. The most popular strategies are depth-first search and limited discrepancy search. Aside from pruning or ordering the children of each node, these algorithms do not adapt their search order to take advantage of information that becomes available during search, such as heuristic scores or leaf costs. We present a framework called best-leaf-first search (BLFS) that uses this additional information to estimate the cost of taking discrepancies in the search tree and then attempts to visit leaves in a best-first order. In this way, BLFS brings the idea of best-first search from shortest path problems to the areas of constraint satisfaction and combinatorial optimization. Empirical results demonstrate that this new dynamic approach results in better search performance than previous static search strategies on two very different domains: structured CSPs and the traveling salesman problem.

The Pancake problem has become a classical combinatorial problem. Different attempts have been made to optimally solve it and/or to derive tighter bounds on the diameter of its state space for a different number of discs. Until very recently, the most successful technique for solving different instances optimally was based on Pattern Databases. Although different approaches have been tried, solutions with Pattern Databases on Pancakes with more than 19 discs have never been reported. In this work, a new technique is introduced which allows the definition of Additive Pattern Databases for solving Pancakes of an arbitrary length. As a result, this technique solves Pancake problems with twice as many discs as the largest ones solved nowadays with other techniques based on Pattern Databases saving up to two orders of magnitude of space.

In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This paper makes two contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic or greedy search. The success of Switch illustrates the potential for further research on specifically suboptimal hierarchical search.

Herein, we employ a genetic algorithm (GA) to obtaining solvers for both the difficult FreeCell puzzle and the slidingtile Discrete puzzles, also known as single-player games, are puzzle. Note that although from a computationalcomplexity an excellent problem domain for artificial intelligence research, point of view the Rush Hour puzzle is harder because they can be parsimoniously described yet (unless NP PSPACE), search spaces induced by typical instances are often hard to solve (Pearl 1984). A well-known, highly of FreeCell tend to be substantially larger than those popular example within the domain of discrete puzzles is the of Rush Hour, and thus far more difficult to solve. This is card game of FreeCell. Another highly popular game is the evidenced by the failure of standard search methods to solve sliding-tile puzzle, the traditional versions of which are the FreeCell, as opposed to their success in solving all 6x6 Rush 15-puzzle (4X4) and the 24-puzzle (5X5). State-of-the-art Hour problems without requiring any heuristics (Hauptman heuristics allow for fast solutions of arbitrary instances of et al. 2009).