Introduction

Intelligent robots must be both proactive and responsive. that requirement is the main challenge facing designers and developers of robot architectures. A robot in an active environment changes that environment in order to meet its goals and it, in turn, is changed by the environment. In this chapter we propose that these concerns can best be addressed by using constraint satisfaction as the design framework. This will allow us to put a firmer technical foundation under various proposals for codes of robot ethics.

Constraint Satisfaction Problems

We will start with what we might call Good Old-Fashioned Constraint Satisfaction (GOFCS). Constraint satisfaction itself has now evolved far beyond GOFCS. However, we initially focus on GOFCS as exemplified in the constraint satisfaction problem (CSP) paradigm. The whole concept of constraint satisfaction is a powerful idea. It arose in several applied fields roughly simultaneously; several researchers, in the early 1970s, abstracted the underlying theoretical model. Simply, many significant sets of problems of interest in artificial intelligence can each be characterized as a CSP. A CSP has a set of variables; each variable has a domain of possible values, and there are various constraints on some subsets of those variables, specifying which combinations of values for the variables involved are allowed (Mackworth 1977). The constraints may be between two variables or among more than two variables. A familiar CSP example is the Sudoku puzzle.