An artificial intelligence system for computer-assisted menu planning

Planning nutritious and appetizing menus is a complex task that researchers have tried to computerize since the early 1960s. We have attempted to facilitate computer-assisted menu planning by modeling the reasoning an expert dietitian uses to plan menus. Two independent expert systems were built, each designed to plan a daily menu meeting the nutrition needs and personal preferences of an individual client. One system modeled rule-based, or logical, reasoning, whereas the other modeled case-based, or experiential, reasoning. The 2 systems were evaluated and their strengths and weaknesses identified. A hybrid system was built, combining the best of both systems. The hybrid system represents an important step forward because it plans daily menus in accordance with a person's needs and preferences; the Reference Daily Intakes; the Dietary Guidelines for Americans; and accepted aesthetic standards for color, texture, temperature, taste, and variety. Additional work to expand the system's scope and to enhance the user interface will be needed to make it a practical tool. Our system framework could be applied to special-purpose menu planning for patients in medical settings or adapted for institutional use. We conclude that an artificial intelligence approach has practical use for computer-assisted menu planning

Integrating CBR and RBR for nutritional menu design

An integrated approach is presented, in which a primarily case-based reasoning (CBR) system for nutritional menu design is enhanced by rule-based reasoning (RBR). In nutritional menu planning, a nutritionist plans a daily menu for a single individual, taking dietary requirements and personal preferences into account. This task requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria. In our approach, CBR is used to satisfy multiple numeric constraints, while RBR allows the introduction of new foods into menus and the performance of "what if" analysis needed for creative design. We developed our approach by combining the strengths of independent CBR and RBR menu planning systems. We believe the approach would extend to other design domains in which both physical constraints and aesthetic considerations are important, such as college course advising, architecture, and new product design

The role of common sense knowledge in menu planning

Planning nutritious and appetizing menus is a task at which experts consistently outperform computer systems, making it a challenging domain for AI research. This paper describes lessons learned through the sequential construction of four expert systems for menu planning. The first of these systems, ESOMP (Expert System on Menu Planning), was built to plan menus for patients on severely restricted low protein diets. A need for common sense in structuring 'sensible' looking meals was identified and addressed via meal patterns and food exchange groups. PRISM (Pattern Regulator for the Intelligent Selection of Menus) expanded upon ESOMP by planning menus to meet a broad range of dietary requirements and personal preferences. Simple knowledge representation structures, implemented in PRISM 1.0, proved inadequate for the expanded task. A hierarchical network structure was developed and implemented in PRISM 2.0 and PRISM 3.0. This structure captures the common sense concept of menu form and describes context-sensitive relationships among menu parts. A major contribution of this paper is showing how to represent common sense knowledge about food and menus in a form amenable to successful menu planning

Indices of Metabolic Dysfunction and Oxidative Stress

Abstract Metabolic alterations are a key player involved in the onset of Alzheimer disease pathophysiology and, in this review, we focus on diet, metabolic rate, and neuronal size differences that have all been shown to play etiological and pathological roles in Alzheimer disease. Specifically, one of the earliest manifestations of brain metabolic depression in these patients is a sustained high caloric intake meaning that general diet is an important factor to take in account. Moreover, atrophy in the vasculature and a reduced glucose transporter activity for the vessels is also a common feature in Alzheimer disease. Finally, the overall size of neurons is larger in cases of Alzheimer disease than that of age-matched controls and, in individuals with Alzheimer disease, neuronal size inversely correlates with disease duration and positively associates with oxidative stress. Overall, clarifying cellular and molecular manifestations involved in metabolic alterations may contribute to a better understanding of early Alzheimer disease pathophysiology