Towards a general theory of action and time
Artificial Intelligence
Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
A logic-based calculus of events
New Generation Computing
Temporal logics in AI: semantical and ontological considerations
Artificial Intelligence
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Exploiting constraints in design synthesis
Exploiting constraints in design synthesis
A critical examination of Allen's theory of action and time
Artificial Intelligence
A survey on temporal reasoning in artificial intelligence
AI Communications
| Hi-index | 0.00 |
This paper describes a knowledge-based temporal representation of state transitions for industrial real-time systems. To allow expression of uncertainty, we shall define fluents as disjuncts of positive/negative time-varying properties. A state of the world is represented as a collection of fluents, which is usually incomplete in the sense that neither the positive form nor the negative form of some properties can be implied from it. The world under consideration is assumed to persist in a given state until an action(s) takes place to effect a transition of it into another state, where actions may either be instantaneous or durative. High-level causal laws are characterized in terms of relationships between actions and the involved world states. An effect completion axiom is imposed on each causal law to guarantee that all the fluents that can be affected by the performance of the corresponding action are governed. This completion requirement is practical for most industrial real-time applications and in fact provides a simple and effective treatment to the so-called frame problem.