Constraint Satisfaction. Computing a blame assignment boils down to computing an interpretation for a constraint list ς. An interpretation µ of a constraint list ς is a mapping from blame identifiers b to records of elements of B = {true, false}, such that all constraints are satisfied. ± We order truth values by true m false and write for the reflexive closure of that
Constraint Satisfaction. Q ¢ b of a predicate. Meta-function τ : v → B (Figure 5.32) translates the λCON values to truth values B by stripping delayed contracts and interpreting the outcome according to JavaScript’s definition of truthy and falsy. A flat contract never blames its context so that b.context is always true. Rule CT-Inversion determines the blame assignment for an object contract. Initially, the subject of an object contract is responsible for returning property values according to their specification, whereas the context is responsible for using all properties according to its specification. This does not require a special constraint. However, after writing a property, the responsibility for the value has changed. Now, the context is responsible for the value that is later returned from the contracted object. Rule CT-Function determines the blame assignment for a function contract b from the blame assignment for the argument and result contracts, which are available through ι1 and ι2. Let’s first consider the subject part. A function satisfies contract b if it satisfies its obligations towards its argument ι1.context and if the argument satisfies its contract then the result satisfies its contract. The first part arises if the function is a higher-order function, which may pass illegal arguments to its function-arguments. The second part is partial (τ v
Constraint Satisfaction. In Encyclopedia of AI, S.C. Xxxxxxx(ed.), pp. 205-211. NCSA. (1995). The Common Gateway Interface. URL:xxxx://xxxxxx.xxxx.xxxx.xxx/cgi. Netscape. (1995). Persistent Client State HTTP Cookies. URL: xxxx://xxxx.xxxxxxxx.xxx/newsref/std/ cookie_spec.html. Xxxxx, X. (1989). Intel's Configurations Expert. The Xxxxx Xxxxxxxx Report, NY:Xxxx Xxxxx. Trilogy Development Group of Austin, Texas. (1994). Salesbuilder: A Commercial Constraint-Based Product Configuration System. Intelligent Software Strategies, 10(1). Xxxxxx, J. R., Xxxxxxxxxx, X. X., Xxxxxxxx, G. T., Xxxxx, X. X., Xxxxxx, S. R., Xxxxxx, X. X., & Xxxxx, X. X. (1993). A Knowledge-Based Configurator That Supports Sales, Engineering, and Manufacturing at AT&T Network Systems. AI Magazine, 14(3), 69-80. LIST OF FIGURES AND TABLES FIGURE 1. Architecture of the Salesman Expert System FIGURE 2. Variables in Men's Wear Case FIGURE 3. Representation of a Product FIGURE 4. Representation of a Customer FIGURE 5. A Constraint and Rule Graph for Men's Wear Case FIGURE 6. Customer Input Screen Using HTML Forms FIGURE 7. Intermediate Specification FIGURE 8. A Final Specification FIGURE 9. Selected Items Display Screen FIGURE 10. Architecture of UNIK-SES in WWW Environment TABLE 1. Comparison of Rule-based Approach and Constraint-based Approach TABLE 2. Contrast of the Configuration Systems Rules about Customers & Products Sales Strategic Rules Customer Database Constraints about Customers & Products Purchasing Support Inference CRSP Engine Product Matcher Product Database Dynamically Generated HTML Document HTML FORMS World Wide Web Xxxxxx FIGURE 1. Architecture of the Salesman Expert System Suit model-name price size material brand color pattern style brightness breasted inventory Shirt model-name price size material brand color pattern brightness collar inventory Slacks model-name price size material brand color pattern style Neck-tie model-name price material brand color pattern inventory Jacket model-name price size material brand color Customer occupation sex income-level age face-color body-type Buying-plan budget-upper-bound budget-lower-bound total-amount purpose decision pattern brightness material-thickness inventory material-thickness inventory height
