Crowdsourcing has revolutionized the process of knowledge building on the web. Wikipedia and StackOverflow are witness to this uprising development. However, the dynamics behind the process of crowdsourcing in the domain of knowledge building is an area relatively unexplored. It has been observed that an ecosystem exists in the collaborative knowledge building environments (KBE)(Chhabra, Iyenga...

In recent years, Bayesian networks have become highly successful tool for diagnosis, analysis, and decision making in real-world domains. We present an efficient algorithm for learning Bayes networks from data. Our approach constructs Bayesian networks by first identifying each node’s Markov blankets, then connecting nodes in a maximally consistent way. In contrast to the majority of work, whic...

Issues dealing with fast, 3-D, collision-free motion planning are discussed, and a fast path planning system under developnlent at NBS i s described. The components of a general motion planner are outlined, and some of their computational aspects are discussed. It is argued that an octree representation of the obstacles in the world leads to fast path planning algorithms. The system we are deve...

This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedyascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for...

The Disjunctive Temporal Problem with Uncertainty (DTPU) is a fundamental problem that expresses temporal reasoning with both disjunctive constraints and contingency. A recent work (Peintner et al, 2007) develops a complete algorithm for determining Strong Controllability of a DTPU. Such a notion that guarantees 100% confidence of execution may be too conservative in practice. In this paper, fo...

This paper presents a new method to pack convex polygons into bins (the nesting problem). To do this polygons are placed in rows within bins using a metaheuristic algorithm (simulated annealing) and by utilising the No Fit Polygon. We show that simulated annealing out performs hill climbing. The nesting algorithm is described in detail, along with various aspects that have been incorporated in ...

Classical methods for Inductive Concept Learning (ICL) rely mostly on using specific search strategies, such as hill climbing and inverse resolution. These strategies have a great exploitation power, but run the risk of being incapable of escaping from local optima. An alternative approach to ICL is represented by Evolutionary Algorithms (EAs). EAs have a great exploration power, thus they have...

We present a new heuristic method to evaluate planning states, which is based on solving a relaxation of the planning problem. The solutions to the relaxed problem give a good estimate for the length of a real solution, and they can also be used to guide action selection during planning. Using these informations, we employ a search strategy that combines Hill-climbing with systematic search. Th...

This paper proposes a generalized method for playfield segmentation of various sport videos. It first estimates the probability density function (pdf) of color components of an image frame. Two hill-climbing schemes, which employ two-dimensional pdf and one-dimensional pdf, respectively, are proposed for clustering. Next, a novel algorithm that utilizes the domain knowledge of sport playfields ...

While attempting to classify partial linear spaces produced during the execution of an extension of Stinson’s hill-climbing algorithm a new problem arises, that of generating all graphical degree sequences that are diamond-free (i.e. have no diamond as subgraph) and satisfy additional constraints. We formalize this new problem, propose a constraint programming solution and list all satisfying d...