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Integrating TCP-Nets and CSPs: The Constrained TCP-Net (CTCP-Net) Model

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Current Approaches in Applied Artificial Intelligence (IEA/AIE 2015)

Abstract

In this paper, a new framework for constraint and preference representation and reasoning is proposed, including the related definitions, algorithms and implementations. A Conditional Preference Network (CP-Net) is a widely used graphical model for expressing the preferences among various outcomes. While it allows users to describe their preferences over variables values, the CP-Net does not express the preferences over the variables themselves, thus making the orders of outcomes incomplete. Due to this limitation, an extension of CP-Nets called Tradeoffs-enhanced Conditional Preference Networks (TCP-Nets) has been proposed to represent the relative importance between variables. Nonetheless, there is no research work reporting on the implementation of TCP-Nets as a solver. Moreover, the TCP-Net only deals with preferences (soft constraints). Hard constraints are not explicitly considered. This is a real limitation when dealing with a wide variety of real life problems including both constraints and preferences. This has motivated us to propose a new model integrating TCP-Nets with the well known Constraint Satisfaction Problem (CSP) framework for constraint processing. The new model, called Constrained TCP-Net (CTCP-Net), has been implemented as a three-layer architecture system using Java and provides a GUI for users to freely describe their problem as a set of constraints and preferences. The system will then solve the problem and returns the solutions in a reasonable time. Finally, this work provides precious information for other researchers who are interested in CSPs and graphical models for preferences from the theoretical and practical aspects.

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Correspondence to Malek Mouhoub .

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Zhang, S., Mouhoub, M., Sadaoui, S. (2015). Integrating TCP-Nets and CSPs: The Constrained TCP-Net (CTCP-Net) Model. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_20

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  • DOI: https://doi.org/10.1007/978-3-319-19066-2_20

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-19066-2

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