Logic-based models are thriving within artificial intelligence. A great number of new logics have been defined, and their theory investigated. Epistemic logics introduce modal operators for knowledge or belief; deontic logics are about norms, and introduce operators of deontic necessity and possibility (i.e., obligation or prohibition). And then we have a much investigated class—temporal logics—to whose application to engineering this special issue is devoted. This kind of formalism deserves increased widespread recognition and application in engineering, a domain where other kinds of temporal models (e.g., Petri nets) are by now a fairly standard part of the modelling toolbox.

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.

In this paper we present a specialized system for temporal reasoning representing possibly uncertain qualitative and quantitative constraints relative to temporal intervals in a homogeneous framework. The underlying logic is that of Allen, which we have extended to include quantitative temporal information. Intervals are still assumed as primitive entities while, to represent metric information, time points are introduced only at the algorithmic level. We illustrate the properties of the temporal representation, the algorithms used for constraint propagation, and their complexity. We show how the system can be used to schedule manufacturing operations in a small factory.

Computer-aided process planning has been recognized as an important tool for coordinating the different operations involved in making the product. While temporal knowledge is central to the design of efficient and reliable process plans, little attention is given to the integration of process planning and temporal processing and reasoning. To fill the void, we propose in this paper a practical approach, which is inspired by the framework of Temporal Constraint Satisfaction Problem (TCSP), to integrate process planning and temporal reasoning. We show that a TCSP formulation is a subset of a formulation using a reified temporal logic, and discuss the advantages of using such a restricted model. To reflect more realistic process planning encountered in real manufacturing environments, we present a model, called n-TCSP, which is a generalization of the TCSP framework. We envision the proposed temporal reasoning framework as one of the modules in the evolving new intelligent computer-aided process planning.

A temporal logic for representing and reasoning on a robotic domain is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal language is a member of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The logic is used to organize the domain actions and plans in a taxonomy. The classification and recognition tasks, together with the subsumption task form the basis for action management. An action/plan description can be automatically classified into a taxonomy; an action/plan instance can be recognized to take place at a certain moment from the observation of what is happening in the world during a time interval.

Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them.

In this paper we present a formal common sense theory of the adoption of perception-based beliefs. We begin with a logical analysis of perception and then consider when perception should lead to belief change. Our theory is intended to apply to perception in humans and to perception in artificial agents at the level of the symbolic interface between a vision system and a belief system. In order to provide a context for our work we relate it to the emerging field of cognitive robotics, give an abstract architecture for an agent which is both embodied and capable of reasoning, and relate this to the concrete architectures of two vision-based surveillance systems.