Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Constraints are a porful general paradigm for representing knowledge in intelligent systems. The standard constraint satisfaction paradigm involves variables over a discrete value domain and constraints which restrict the solutions to allod value combinations. This standard paradigm is inapplicable to problems which are either:#R##N##R##N#(a) mixed, involving both numeric and discrete variables, or#R##N##R##N#(b) conditional,1 containing variables whose existence depends on the values chosen for other variables, or#R##N##R##N#(c) both, conditional and mixed.#R##N##R##N# present a general formalism which handles both exceptions in an integral search framework.  solve conditional problems by analyzing dependencies beten constraints that enable us to directly compute all possible configurations of the CSP rather than discovering them during search. For mixed problems,  present an enumeration scheme that integrates numeric variables with discrete ones in a single search process. Both techniques take advantage of enhanced propagation rule for numeric variables that results in tighter labelings than the algorithms commonly used. From real world examples in configuration and design,  identify several types of mixed constraints, i.e. constraints defined over numeric and discrete variables, and propose new propagation rules in order to take advantage of these constraints during problem solving.
['Esther Gelle', 'Boi Faltings']
I am writing a research paper on a idea Solving Mixed and Conditional Constraint Satisfaction Problems
[{"from": "human", "value": "I am writing a research paper on a idea Solving Mixed and Conditional Constraint Satisfaction Problems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Constraints are a porful general paradigm for representing knowledge in intelligent systems. The standard constraint satisfaction paradigm involves variables over a discrete value domain and constraints which restrict the solutions to allod value combinations. This standard paradigm is inapplicable to problems which are either:#R##N##R##N#(a) mixed, involving both numeric and discrete variables, or#R##N##R##N#(b) conditional,1 containing variables whose existence depends on the values chosen for other variables, or#R##N##R##N#(c) both, conditional and mixed.#R##N##R##N# present a general formalism which handles both exceptions in an integral search framework.  solve conditional problems by analyzing dependencies beten constraints that enable us to directly compute all possible configurations of the CSP rather than discovering them during search. For mixed problems,  present an enumeration scheme that integrates numeric variables with discrete ones in a single search process. Both techniques take advantage of enhanced propagation rule for numeric variables that results in tighter labelings than the algorithms commonly used. From real world examples in configuration and design,  identify several types of mixed constraints, i.e. constraints defined over numeric and discrete variables, and propose new propagation rules in order to take advantage of these constraints during problem solving."}]