Backtracking algorithms for disjunctions of temporal constraints

Stergiou, Kostas; Koubarakis, Manolis

doi:10.1016/s0004-3702(00)00019-9

Cited by 117 publications

(113 citation statements)

References 39 publications

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“…Although we apply several checks that can detect basic types of inconsistencies in this case, a full verification would require a technique that can handle disjunctions of non-binary constraints. Even though suitable approaches for this task have been described in the literature [28,29], the problem of fully verifying a guideline that includes unordered sequential activities is NP hard. As Asbru is currently the only representation format that considers unordered sequential activities, we chose to integrate them in the verification process only through partial checks.…”

Section: Resultsmentioning

confidence: 99%

“…We relied on their work to keep our approach simple: The extra effort of using a combined approach did not seem justifiable merely to support the constraints "=" and "≤" - 7 -flow cannot be tackled, as Dechter et al only support binary constraints. If unordered sequential activities are to be considered, approaches that permit the representation of disjunctive non-binary temporal constraints such as the frameworks described by Stergiou and Koubarakis [29] or Staab [28] must be applied. However, the problem of verifying a guideline that includes unordered sequential activities (and thus disjunctive non-binary temporal constraints) is NP hard, as opposed to an effort of O(n 3 ) otherwise.…”

Section: Related Work On the Verification Of Guidelinesmentioning

confidence: 99%

“…To incorporate the unordered sequential execution of plans, we need an approach that can handle non-binary constraints, such as the framework proposed by Stergiou and Koubarakis [29]. They consider temporal constraints of the form X 1 -Y 1 ≤ r 1 ∨ …∨ X n -Y n ≤ r n , where X 1 , …, X n , Y 1 , …, Y n are variables and r 1 , …, r n are constants.…”

Section: Handling Asbru's Operator For Unordered Sequential Plan Exementioning

confidence: 99%

See 2 more Smart Citations

Verification of temporal scheduling constraints in clinical practice guidelines

Duftschmid¹,

Miksch²,

Gall³

2002

Artificial Intelligence in Medicine

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The computerization of clinical practice guidelines is a significant scientific challenge for the medical informatics community. One frequently reported factor hindering this objective is the existence of deficiencies within guideline knowledge. In this paper, we focus on the detection of flaws within temporal scheduling constraints. Temporal scheduling constraints are important elements of therapy management, and are frequently incorporated in clinical practice guidelines. We present a suitable verification method that is based on calculating the minimal network of temporal constraints on the execution of guideline activities. Our method serves three purposes: (1) it checks whether temporal scheduling constraints are consistent with scheduling constraints implied by control flow operators and the hierarchical structuring of a guideline; (2) it yields suggestions for an equivalent, yet more explicit representation of non-minimal constraints; (3) it can be used by the guideline interpreter to assemble feasible time intervals for the execution of each guideline activity.We evaluate our approach by applying it to a guideline specified in the Asbru language. For this purpose, we implemented a prototype verifier. Although we concentrate on the guideline representation language Asbru as the demonstration medium of our method within this paper, our approach can be reused to verify several alternative guideline-representation formats.

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Section: Resultsmentioning

confidence: 99%

Section: Related Work On the Verification Of Guidelinesmentioning

confidence: 99%

Section: Handling Asbru's Operator For Unordered Sequential Plan Exementioning

confidence: 99%

See 1 more Smart Citation

Verification of temporal scheduling constraints in clinical practice guidelines

Duftschmid¹,

Miksch²,

Gall³

2002

Artificial Intelligence in Medicine

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“…ALT ALT ALT ALT ALT ALT [7,7] [0,0] Nested TNAs can be seen from the point of view of disjunctive temporal networks [13] so a similar solving approach can be applied to obtain a consistent network. First, we find all solutions to the nested P/A assignment problem.…”

Section: Proofmentioning

confidence: 99%

“…These relations are frequently modeled using temporal networks where nodes correspond to activities and arcs are annotated by the temporal relations between activities. Current temporal networks handle well temporal information including disjunction of temporal constraints [13] or uncertainty [4]. Several other extensions of temporal networks appeared recently such as resource temporal networks [10] or disjunctive temporal networks with finite domain constraints [11].…”

Section: Introductionmentioning

confidence: 99%

Temporal Reasoning in Nested Temporal Networks with Alternatives

Barták

Čepek

Hejna

2008

Lecture Notes in Computer Science

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Abstract. Temporal networks play a crucial role in modeling temporal relations in planning and scheduling applications. Temporal Networks with Alternatives (TNAs) were proposed to model alternative and parallel processes in production scheduling, however the problem of deciding which nodes can be consistently included in such networks is NP-complete. A tractable subclass, called Nested TNAs, can still cover a wide range of real-life processes, while the problem of deciding node validity is solvable in polynomial time. In this paper, we show that adding simple temporal constraints (instead of precedence relations) to Nested TNAs makes the problem NP-hard again. We also present several complete and incomplete techniques for temporal reasoning in Nested TNAs.

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Constraint Programming

Mathieu¹,

Keisu²

2000

Logic‐Based Methods for Optimization

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Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, operations research, algorithms, graph theory and elsewhere. The basic idea in constraint programming is that the user states the constraints and a general purpose constraint solver is used to solve them. Constraints are just relations, and a constraint satisfaction problem (CSP) states which relations should hold among the given decision variables. More formally, a constraint satisfaction problem consists of a set of variables, each with some domain of values, and a set of relations on subsets of these variables. For example, in scheduling exams at an university, the decision variables might be the times and locations of the different exams, and the constraints might be on the capacity of each examination room (e.g., we cannot schedule more students to sit exams in a given room at any one time than the room's capacity) and on the exams scheduled at the same time (e.g., we cannot schedule two exams at the same time if they share students in common). Constraint solvers take a real-world problem like this represented in terms of decision variables and constraints, and find an assignment to all the variables that satisfies the constraints. Extensions of this framework may involve, for example, finding optimal solutions according to one or more optimization criterion (e.g., minimizing the number of days over which exams need to be scheduled), finding all solutions, replacing (some or all) constraints with preferences, and considering a distributed setting where constraints are distributed among several agents.Constraint solvers search the solution space systematically, as with backtracking or branch and bound algorithms, or use forms of local search which may be incomplete. Systematic method often interleave search (see Section 4.3) and inference, where inference consists of propagating the information contained in one constraint to the neighboring constraints (see Section 4.2). Such inference reduces the parts of the search space that need to be visited. Special propagation procedures can be devised to suit specific constraints (called global constraints), which occur often in real life. Such global constraints are an important component in the success of constraint pro- Backtracking SearchA backtracking search for a solution to a CSP can be seen as performing a depth-first traversal of a search tree. This search tree is generated as the search progresses. At a

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Backtracking algorithms for disjunctions of temporal constraints

Cited by 117 publications

References 39 publications

Verification of temporal scheduling constraints in clinical practice guidelines

Verification of temporal scheduling constraints in clinical practice guidelines

Temporal Reasoning in Nested Temporal Networks with Alternatives

Constraint Programming

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