Temporal representation and reasoning

1992

Artificial Intelligence Review, 2001

Temporal reasoning started to be considered as a subject of study in artificial intelligence in the late '70s. Since that several ways to represent and use temporal knowledge have been suggested. As a result of that there are several formalisms capable of coping with temporal notions in some way or other. They range from isolated proposals to complexsystems where the

Synthesis Lectures on Artificial Intelligence and Machine Learning, 2014

Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. is book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. e book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. is book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.

Cognitive science, 1982

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first-order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It may be possible to implement a temporal-inference machine based on this logic, which keeps track of several "maps" of a time line, one per possible history.

Lecture Notes in Computer Science, 2004

UNU/IIST is jointly funded by the Governor of Macau and the governments of the People's Republic of China and Portugal through a contribution to the UNU Endownment Fund. As well as providing two-thirds of the endownment fund, the Macau authorities also supply UNU/IIST with its office premises and furniture and subsidise fellow accommodation. The mission of UNU/IIST is to assist developing countries in the application and development of software technology. UNU/IIST contributes through its programmatic activities:

Temporal Information Systems in Medicine, 2010

In this chapter, the reader is guided through the basic notions of time and temporal information and is presented with some important, general approaches to represent and reason about temporal information. Simple medical examples are used to help the reader to understand the advantages and limitations of the different approaches.

10th International Symposium on Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic. Proceedings., 2003

In many application areas, including planning, workflow, guidelines and protocol management, the description of the domain requires the use of part-of relations between events, the modeling of periodic repetitions and the treatment of "standard" temporal constraints between such events. Events in plans, workflows etc. represent "classes", in the sense that they can be instantiated to specific executions of the plan, guideline etc. Of course, such executions must respect (i.e., be consistent with) the temporal constraints explicitly or implicitly (e.g., by the part-of relation) conveyed by the class descriptions. In this paper, we propose a tractable domain-independent temporal server dealing with the above phenomena. We first sketch a representation formalism coping with partof and instance-of relations, periodicity and temporal constraints, and then we describe two algorithms to deal with inheritance and to perform temporal consistency checking.

Electronic Proceedings in Theoretical Computer Science, 2017

Handbook of Modal Logic, 2007

Encyclopedia of Database Systems, 2009

Temporal logical models refer to the logical structure of data that captures the temporal behavior and operations over such structures. The term "logical" is used to distinguish such temporal structures from the physical storage organization and implementation. For example, the behavior of temporal events and operations over them can be described logically in a way that is independent of the physical structure (e.g. linked lists) or indexing of the events. Temporal logical models include concepts of data values that are collected or are changed over time, such as continuous physical phenomena, a series of discrete events, and interval data over time. The challenge is one of having a single comprehensive model that captures this diversity of behavior. In the 1980's several researchers focused on dealing with temporal data, both on the modeling concepts and on physical organization and indexing of temporal data. This led to the temporal database field to be established, and several books were written or edited on the subject (for example ). Since then, the subject continues to appear in specific application domains, or in combination with other concepts, such as spatio-temporal databases, and managing streaming data.