Reasoning with Numeric and Symbolic Time Information

Mouhoub, Malek

doi:10.1023/b:aire.0000007179.60276.39

Cited by 19 publications

(28 citation statements)

References 16 publications

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“…MPP relations express in a quantitative imprecise way the length of time elapsing between two points using any temporal unit defined in the system (seconds, minutes, hours, days, months and years). Some authors have defined a semantic for metric distances between intervals (see, for example, Badaloni & Berati, 1996;Mouhoub, 2004). In any case, these distances can be modeled in FuzzyTIME by using the variables of the beginning and end of each interval, and establishing a relation between them.…”

Section: Metric Relationsmentioning

confidence: 99%

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Avian influenza: Temporal modeling of a human to human transmission case

Campos

Palma

Marín

et al. 2011

Expert Systems with Applications

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Section: Metric Relationsmentioning

confidence: 99%

“…We could also have used interval-to-point and interval-to-interval metric temporal relations (see, for example, Badaloni & Berati, 1996;Mouhoub, 2004). Expression 8 is an example of the first type (distance between a point (death) and an interval (funeral)).…”

Section: Comments About Modelingmentioning

confidence: 99%

Avian influenza: Temporal modeling of a human to human transmission case

Campos

Palma

Marín

et al. 2011

Expert Systems with Applications

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“…This model extends the well known qualitative temporal networks, STP (Dechter, Meiri, and Pearl 1991) and DTP (Tsamardinos and Pollack 2002) by adding instantaneous events (called observation nodes) representing conditional constraints and attaching labels to all nodes to indicate the different situations. We adopt both the CCSP (Mittal and Falkenhainer 1990) and the composite CSP paradigms and extend the modeling framework TemPro (Mouhoub 2004) by including conditional temporal constraints and composite temporal events as shown in introduction. TemPro will then have the ability to transform constraint problems involving numeric information, symbolic information, conditional constraints and composite variables into the CCTCSP we have described in introduction.…”

Section: Conditional and Composite Temporal Constraint Satisfaction Pmentioning

confidence: 99%

“…While a considerable research work has been concerned with reasoning on the metric or the symbolic aspects of time (respectively through metric or qualitative networks), little work such as (Kautz and Ladkin 1991;Meiri 1996;Ghallab and Laruelle 1994;Thornton et al 2004) has been developed to manage both types of information. In (Mouhoub 2004), we have developed a temporal model, TemPro, based on Allen's interval algebra (Allen 1983) and a discrete representation of time, to express numeric and symbolic time information in terms of qualitative and quantitative temporal constraints. More precisely, TemPro translates an application involving numeric and symbolic temporal information into a binary CSP 1 called Temporal CSP (or TCSP 2 ) where variables are temporal events defined on domains of numeric intervals and binary constraints between variables correspond to disjunctions of Allen primitives (Allen 1983).…”

Section: Introductionmentioning

confidence: 99%

Conditional and composite temporal CSPs

Mouhoub

Sukpan

2010

Appl Intell

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In order to deal with dynamic CSPs where the information regarding any possible change is known a priori and can thus be enumerated beforehand, conditional constraints and composite variables have been studied in the past decade. Indeed, these two concepts allow the addition of variables and their related constraints in a dynamic manner during the resolution process. More precisely, a conditional constraint restricts the participation of a variable in a feasible scenario while a composite variable allows us to express a disjunction of variables where only one will be added to the problem to solve. In order to deal with a wide variety of real life applications under temporal constraints, we present in this paper a unique temporal CSP framework including numeric and symbolic temporal information, conditional constraints and composite variables. We call this model, a Conditional and Composite Temporal CSP (or CCTCSP). To solve the CCTCSP we propose several search techniques based on constraint propagation. In order to assess the efficiency in time of these search techniques, experimental tests have been conducted on randomly generated CCTCSPs. The results demonstrate the superiority of a variant of the Maintaining Arc Consistency (MAC) (that we call MAX+) over the other constraint propagation strategies (FC and its variant) for both consistent and inconsistent problems.

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“…In Mouhoub andSukpan (2005a, 2005b) we have proposed two methods for solving CCTCSPs. These two methods are respectively based on constraint propagation and stochastic local search.…”

Section: Managing Conditional Constraints and Composite Variablesmentioning

confidence: 99%

Managing Temporal Constraints with Preferences

Mouhoub

Sukpan

2008

Spatial Cognition & Computation

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Preferences in temporal problems are common but significant in many real world applications. In this paper, we extend our temporal reasoning framework, managing numeric and symbolic information, in order to handle preferences. Unlike the existing models managing single temporal preferences, ours supports four types of preferences, namely: numeric and symbolic temporal preferences, composite preferences and conditional preferences. This offers more expressive power in representing a wide variety of temporal constraint problems. The preferences are considered here as a set of soft constraints using a c-semiring structure with combination and projection operators. Solving temporal constraint problems with preferences consists in finding a solution satisfying all the temporal constraints while optimizing the preference values. This is handled by a variant of the branch and bound algorithm, we propose in this paper, and where constraint propagation is used to improve the time efficiency. Experimental tests, we conducted on randomly generated temporal constraint problems with preferences, favor a variant of MAC as the constraint propagation strategy that should be used within the branch and bound algorithm.

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Reasoning with Numeric and Symbolic Time Information

Cited by 19 publications

References 16 publications

Avian influenza: Temporal modeling of a human to human transmission case

Avian influenza: Temporal modeling of a human to human transmission case

Conditional and composite temporal CSPs

Managing Temporal Constraints with Preferences

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