Handling negative disjunction constraints (or_not_holds) in FLUX

FLUX is a constraint logic programming language based on Fluent Calculus, using which agents can reason logically from their actions and sensor information in incomplete states. The incomplete state is encoded by the constraints in Constraint Handling Rules (CHRs). However, the existing constrains in FLUX are not complete to cover all Fluent Calculus state formulas, which to some extent restricts the range of applications of FLUX. We address this problem by adding negative disjunction constraints into FLUX, which enhances the ability of FLUX to express incomplete states, and the correctness is proved using the semantics of the Fluent Calculus.

I. Introduction

One of the most challenging problems in the research area of artificial intelligence, is to design robots that reason logically about their actions and sensor information in the presence of incomplete states. Logic programming languages based on Situation Calculus, such as Golog, ConGolog adopt the principle of regression. As a consequence, whenever a condition is evaluated in an agent program the entire history of actions is involved in the computation. This requires ever increasing computational effort as the agent proceeds, so regression does not scale up well to long-term agent control. The logic programming method FLUX, based on Fluent Calculus, on the other hand, takes the principle of progression. The advantage of progression is that a condition can be evaluated directly against the current state. Therefore, the reasoning efficiency of FLUX is superior to Golog and ConGolog [1]–[2].