We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or in short as IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experimen...

With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient te...

The paper presents and evaluates the power of a new framework for optimization in graphical models, based on AND/OR search spaces. The virtue of the AND/OR representation of the search space is that its size may be far smaller than that of a traditional OR representation. We develop our work on Constraint Optimization Problems (COP) and introduce a new generation of depth-first Branch-and-Bound...

The paper presents and evaluates the power of limited memory best-first search over AND/OR spaces for optimization tasks in graphical models. We propose Recursive Best-First AND/OR Search with Overestimation (RBFAOO), a new algorithm that explores the search space in a best-first manner while operating with restricted memory. We enhance RBFAOO with a simple overestimation technique aimed at min...

The paper illustrates a Decision Support System (DSS) for multi-reservoir water resource systems, named WARGI (Water Resources system optimization aided by Graphical Interface). WARGI was upgraded in the SEDEMED II Project to implement a mixed approach that uses optimization and simulation techniques to select the best combination of proactive and reactive measures under drought conditions. The...

Humans and animals are capable of overcoming complex terrain challenges with graceful and agile movements. One of the key ingredients for such complex behaviors is motion coordination to exploit passive dynamics. We present a direct collocation trajectory optimization to find optimal control policy and generate optimal trajectory for the swing up motion of a gymnast on high bar. Notwithstanding...

Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding ...

This paper presents an object-oriented software system that implements a cuckoo search (CS) metaheuristic for unconstrained optimization problems. Yang and Deb developed cuckoo search algorithm in MATLAB and tested it on some standard benchmark functions as well as on some engineering optimization problems where it showed promising results. We developed our algorithm in JAVA programming languag...

The Tarski algebra provides an algebraic foundation for object-based query languages. This is demonstrated by showing how queries expressed in a graph-oriented query language (based on the functional data model) can be translated into the Tarski algebra. The graphical representation of queries in combination with the Tarski algebra is a convenient mechanism to study optimization in the context ...

This paper deals with preference elicitation and preference-based optimization in the context of multiattribute utility theory under certainty. We focus on the generalized additive decomposable utility model which allows interactions between attributes while preserving some decomposability. We first present a systematic elicitation procedure for such utility functions. This procedure relies on ...