Nutritional Menu Planning: A Hybrid Approach and Preliminary Tests

Menu planning is a process appearing to be straightforward but many complexities arise when it is tried to be solved by computer means. Actually, although there is evidence of previous work since 50 years ago, at present there is no wide know tool which can solve this task in an automated manner. Also, not all proposals deal with full recipes along with considering the user food preferences. In this paper we pro- pose a system architecture based on hybrid optimization: a rst module based on mathematical programming, a well known robust approach to this problem; and a second module based on belief merging, a lesser known framework aimed to combine the nutrition scientist advices and policies along with the user food desires. The association of numerical and symbolic approaches will allow us to generate of a more agreeable menu. In order to illustrate our proposal, we present a motivating example detailing the main aspects of the system.

[1]  Joseph L. Balintfy.  Menu planning by computer , 1964, CACM.

[2]  E F Eckstein.  Menu planning by computer: the random approach. , 1967, Journal of the American Dietetic Association.

[3]  Eckstein Ef.  Menu planning by computer: the random approach. , 1967 .

[4]  Sarit Kraus,et al.  Combining Knowledge Bases Consisting of First Order Theories , 1991, ISMIS.

[5]  D. Sklan,et al.  Diet planning for humans using mixed-integer linear programming , 1993, British Journal of Nutrition.

[6]  L. A. Quinn,et al.  A recipe-based, diet-planning modelling system , 1995, British Journal of Nutrition.

[7]  Peter Z. Revesz,et al.  On the Semantics of Arbitration , 1997, Int. J. Algebra Comput..

[8]  Sébastien Konieczny,et al.  On the Logic of Merging , 1998, KR.

[9]  L Sterling,et al.  An artificial intelligence system for computer-assisted menu planning. , 1998, Journal of the American Dietetic Association.

[10]  Marco Schaerf,et al.  Arbitration (or How to Merge Knowledge Bases) , 1998, IEEE Trans. Knowl. Data Eng..

[11]  Alberto O. Mendelzon,et al.  Knowledge Base Merging by Majority , 1999 .

[12]  Remo Pareschi,et al.  Dynamic Worlds: From the Frame Problems to Knowledge Management , 1999 .

[13]  Rudolf Kruse,et al.  Fusion: General concepts and characteristics , 2001, Int. J. Intell. Syst..

[14]  G. Kozmann,et al.  A Novel Artificial Intelligence Method for Weekly Dietary Menu Planning , 2005, Methods of Information in Medicine.

[15]  Barbara Koroušić Seljak,et al.  Computer-based dietary menu planning , 2006 .

[16]  Maria del Pilar Pozos Parra,et al.  Model-based belief merging without distance measures , 2007, AAMAS '07.

[17]  Maria del Pilar Pozos Parra,et al.  Partial Satisfiability-Based Merging , 2007, MICAI.

[18]  Maria del Pilar Pozos Parra,et al.  Implementing PS-Merge Operator , 2009, MICAI.

[19]  Jerusa Marchi,et al.  Prime forms and minimal change in propositional belief bases , 2010, Annals of Mathematics and Artificial Intelligence.

[20]  Sébastien Konieczny,et al.  Logic Based Merging , 2011, J. Philos. Log..

[21]  Salil P. Vadhan,et al.  Computational Complexity , 2005, Encyclopedia of Cryptography and Security.

[22]  Weiru Liu,et al.  Tools for Finding Inconsistencies in Real-world Logic-based Systems , 2012, STAIRS.

[23]  F. Azizi,et al.  Leemoo, a Dietary Assessment and Nutritional Planning Software, Using Fuzzy Logic , 2013, International journal of endocrinology and metabolism.

[24]  Sandrine Kott and Joëlle Droux.  Globalizing social rights : the international labour organization and beyond , 2013 .

[25]  L. H. Hecktheuer,et al.  Good food preparation practices in households: A review , 2014 .