Extending CLP(FD) with Interactive Data Acquisition for 3D Visual Object Recognition

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ABSTRACT This paper addresses the 3D object recognition problem modelled as a Constraint Satisfaction Problem. In this setting, each object view can be modelled as a constraint graph where nodes are object parts and constraints are topological and geometrical relationships among them. By modelling the problem as a CSP, we can recognize an object when all constraints are satisfied by exploiting results from the CSP field. However, in classical CSPs variable domains have to be statically defined at the beginning of the constraint propagation process. Thus, not only feature acquisition should be completed before the constraint solving process starts, but all image features should be extracted even if not belonging to significant image parts. In visual applications, this requirement turns out to be inefficient since visual features acquisition is a very time consuming task. We present an Interactive Constraint Satisfaction model for problems where variable domains may not be completely known at...

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    ABSTRACT: Constraint logic programming (CLP) is a multidisciplinary research area which can be located between Artificial Intelligence, Operation Research, and Programming Languages, and has to do with modeling, solving, and programming real-life problems which can be described as a set of statements (the constraints) which describe some relationship between the problem’s variables. This survey paper gives a brief introduction to C(L)P, presents a (necessarily partial) state of the art in CLP research and applications, points out some promising directions for future applications, and discusses how to cope with current research challenges.
    New Trends in Contraints, Joint ERCIM/Compulog Net Workshop, Paphos, Cyprus, October 25-27, 1999, Selected Papers; 01/1999
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    ABSTRACT: Constraint Satisfaction techniques have been recognized to be effective tools for increasing the efficiency of least commitment planners. We focus on least commitment on variable binding. A constraint based approach for this issue has been previously proposed by Yang and Chan [21]. In this setting, the planning problem is mapped onto a Constraint Satisfaction Problem. Its variables represent domain objects and are defined on a finite domain of values; constraints remove inconsistent values from variable domains through constraint propagation. In many applications, however, it is not always convenient, if possible at all, to know in advance all objects belonging to variable domains. Thus, domain values should be retrieved during the plan construction only when needed. The interesting point is that data acquisition for each variable can be guided by the constraint (or the constraints) imposed on the variable itself, in order to retrieve only consistent values. For this purpose, we have extended a Partial Order Planner performing least commitment on variable binding. This extension can cope with incomplete knowledge. We use the Interactive Constraint Satisfaction framework defined in [12] in order to exploit the efficiency deriving from constraint propagation and the possibility of acquiring the domain knowledge during the plan construction. Experimental results and comparisons with related approaches show the effectiveness of the proposed technique. KeywordsPartial Order Planning-Least commitment-Variable Binding-Incomplete Knowledge-Interactive Constraint Satisfaction Techniques
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    ABSTRACT: In classical CLP(FD) systems, domains of variables are completely known at the beginning of the constraint propagation process. However, in systems interacting with an external environment, acquiring the whole domains of variables before the beginning of constraint propagation may cause waste of computation time, or even obsolescence of the acquired data at the time of use. For such cases, the Interactive Constraint Satisfaction Problem (ICSP) model has been proposed as an extension of the CSP model, to make it possible to start constraint propa- gation even when domains are not fully known, performing acquisition of domain elements only when necessary and without the need to restart propagation after every acquisition. In this paper, we present a two sorted CLP language to express and solve ICSPs, and its implementation in the Constraint Handling Rules (CHR) language, a declarative language particularly suitable for high level implementation of constraint solvers.

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Jun 1, 2014