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.

As of the writing of this abstract, the first stage is complete with respect to my Heuristic search is a technique used pervasively in the fields dissertation. The second stage will focus on the setting of of artificial intelligence, automated planning and operations off-line heuristic search where the entire plan is synthesized research to solve a wide range of problems from planning before any actions are executed. I will be near the completion military deployments to planning tasks for a robot that must of my work on second stage by the time of the AAAI clean a messy kitchen. An automated agent can use heuristic doctoral consortium. The third and final stage will focus on search to construct a plan that, when executed, will achieve planning under time pressure when planning and execution a desired task. The search algorithm explores different sequences of actions may happen concurrently. The remainder of this of actions that the agent can execute, looking for a abstract discusses these three topics in more detail.

However, this approach usually requires expert users to write individual transformations for each data source manually. Figure 2: Delete grammar A variety of work (Kandel et al. 2011; Raman and Hellerstein 2001; Liang, Jordan, and Klein 2010) tries to take advantage of user input to solve the transformation problem, cally learn transformation rules through examples. As shown but these methods either cannot learn rules from training in Figure 1, a user might want to reverse the order of the date data or need the training data to contain all the intermediate and use hyphens to replace slashes. The user would just provide steps. We have developed an approach where the user the system with an example "30/07/2010" and "2010-only needs to provide the target value as an example.

In looking back on the last five to ten years of work in heuristic search a few trends emerge. First, there has been a broadening of research topics studied. Second, there has been a deepened understanding of the theoretical foundations of search. Third, and finally, there have been increased connections with work in other fields. This paper, corresponding to a AAAI 2012 invited talk on recent work in heuristic search, highlights these trends in a number of areas of heuristic search. It is our opinion that the sum of these trends reflects the growth in the field and the fact that heuristic search has come of age.

I provide a personal view of some of the major research challenges in the area of combinatorial search. These include solving and playing games with chance, hidden information, and multiple players, optimally solving larger instances of well-known single-agent toy problems, applying search techniques to more realistic problem domains, analyzing the time complexity of heuristic search algorithms, and capitalizing on advances in computing hardware, such as very large external memories and multi-core processors.

This paper provides a broad overview of the situation in the area of Parallel Search with a specific focus on Parallel SAT Solving. A set of challenges to researchers is presented which, we believe, must be met to ensure the practical applicability of Parallel SAT Solvers in the future. All these challenges are described informally, but put into perspective with related research results, and a (subjective) grading of difficulty for each of them is provided.

In order for robots to intelligently perform tasks with humans, they must be able to access a broad set of background knowledge about the environments in which they operate. Unlike other approaches, which tend to manually define the knowledge of the robot, our approach enables robots to actively query the World Wide Web (WWW) to learn background knowledge about the physical environment. We show that our approach is able to search the Web to infer the probability that an object, such as a "coffee,'' can be found in a location, such as a "kitchen.'' Our approach, called ObjectEval, is able to dynamically instantiate a utility function using this probability, enabling robots to find arbitrary objects in indoor environments. Our experimental results show that the interactive version of ObjectEval visits 28% fewer locations than the version trained offline and 71% fewer locations than a baseline approach which uses no background knowledge.

Robotic exploration is an on-line problem in which autonomous mobile robots incrementally discover and map the physical structure of initially unknown environments. Usually, the performance of exploration strategies used to decide where to go next is not compared against the optimal performance obtainable in the test environments, because the latter is generally unknown. In this paper, we present a method to calculate an approximation of the optimal (shortest) exploration path in an arbitrary environment. We consider a mobile robot with limited visibility, discretize a two-dimensional environment with a regular grid, and formulate a search problem for finding the optimal exploration path in the grid, which is solved using A*. Experimental results show the viability of our approach for realistically large environments and its potential for better assessing the performance of on-line exploration strategies.

We study the symmetric rendezvous search problem in which two robots that are unaware of each other’s locations try to meet as quickly as possible. In the symmetric version of this problem, the robots are required to execute the same strategy. First, we present a symmetric rendezvous strategy for the robots that are initially placed on the open plane and analyze its competitive performance. We show that the competitive complexity of our strategy is O ( d / R ) where d is the initial distance between the robots and R is the communication radius. Second, we extend the symmetric rendezvous strategy for the open plane to unknown environments with polygonal obstacles. The extended strategy guarantees a complete coverage of the environment. We analyze the strategy for square, translating robots and show that the competitive ratio of the extended strategy is O ( d / D ) where D is the length of the sides of the robots. In obtaining this result, we also obtain an upper bound on covering arbitrary polygonal environments which may be of independent interest.

The paper focuses on finding the m best solutions to combinatorial optimization problems using Best-First or Branchand- Bound search. Specifically, we present m-A*, extending the well-known A* to the m-best task, and prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since Best-First algorithms have memory problems, we also extend the memoryefficient Depth-First Branch-and-Bound to the m-best task. We extend both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments with 5 variants of Best-First and Branch-and-Bound confirm that Best-First is largely superior when memory is available, but Branch-and-Bound is more robust, while both styles of search benefit greatly when the heuristic evaluation function has increased accuracy.