PhD in Computer Science

PhD Course on “Temporal constraint networks"

Period
November 13 - December 20

Academic staff
Roberto Posenato

Series to which this belongs

33° ciclo
34° ciclo
35° ciclo

Description

In many area of AI, natural language cognition, scheduling, planning, qualitative reasoning, etc.,
representing and reasoning about time is an important research topic.
A temporal reasoning system should consist of a temporal knowledge base, a service to check
its check its consistency, and an inference mechanism capable of discovering new information.
Constraint processing frameworks and techniques are usually considered for such temporal
reasoning issues.
A constraint processing framework works on entities and constraints. Two type of temporal
entities are considered: points and intervals. Intervals correspond to time periods during which
events occur or proposition hold, and points represent beginning and ending points of some
events. Temporal statements like “beginning of A precedes the end of B” or “the duration of task
A has to be in the range [1, 10]” are treated as constraints on the location of entities along the
time line. There are two type of constraints: qualitative and quantitative. Qualitative constraints
specify the relative position of paired entities, and quantitative constraints place absolute
bounds or restrict the temporal distance between points.
In this short course, after an introduction to the field of constraint processing, the most relevant
graph based temporal constraint models are presented and analyzed to give the state of art in
such field.

Attachments

Documents
  • pdf   Flyer   (pdf, it, 339 KB, 12/11/19)