Introduction to algorithms
Artificial Intelligence - Special issue on knowledge representation
Backtracking algorithms for disjunctions of temporal constraints
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
SAT-Based Procedures for Temporal Reasoning
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Efficient solution techniques for disjunctive temporal reasoning problems
Artificial Intelligence
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Temporal constraint reasoning with preferences
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Temporal preference optimization as weighted constraint satisfaction
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Fast (incremental) algorithms for useful classes of simple temporal problems with preferences
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the modelling and optimization of preferences in constraint-based temporal reasoning
Artificial Intelligence
Solving disjunctive temporal problems with preferences using maximum satisfiability
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
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In this paper, we provide a polynomial-time algorithm for solving an important class of metric temporal problems that involve simple temporal constraints between various events (variables) and piecewise constant preference functions over variable domains. We are given a graph G = (χ, ε) where χ = {X0, X1... Xn} is a set of events (X0 is the "beginning of the world" node and is set to 0 by convention) and e = (Xi, Xj) ∈ ε. annotated with the bounds [LB(e), UB(e)], is a simple temporal constraint between Xi and Xj indicating that Xj must be scheduled between LB(e) and UB(e) seconds after Xi is scheduled (LB(e) ≤ UB(e)). A family of stepwise constant preference functions F = {fxi (t) : R → R} specifies the preference attached with scheduling Xi at time t. The goal is to find a schedule for all the events such that all the temporal constraints are satisfied and the sum of the preferences is maximized. Our polynomial-time algorithm for solving such problems (which we refer to as extended simple temporal problems (ESTPs)) has important consequences in dealing with limited forms of disjunctions and preferences in metric temporal reasoning that would otherwise require an exponential search space.