3,981 research outputs found
The Mathematics of Dispatchability Revisited
Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatchable STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, the dispatchability property has both theoretical and practical interest. One thing that hampers further work in this area is the underdeveloped theory. The existing definitions are expressed in terms of algorithms, and are less suitable for mathematical proofs. In this paper, we develop a new formal theory of dispatchability in terms of execution sequences. We exploit this to prove a characterization of dispatchability involving the structural properties of the STN graph. This facilitates the potential application of the theory to uncertainty reasoning
Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case
In real-life temporal scenarios, uncertainty and preferences are often
essential and coexisting aspects. We present a formalism where quantitative
temporal constraints with both preferences and uncertainty can be defined. We
show how three classical notions of controllability (that is, strong, weak, and
dynamic), which have been developed for uncertain temporal problems, can be
generalized to handle preferences as well. After defining this general
framework, we focus on problems where preferences follow the fuzzy approach,
and with properties that assure tractability. For such problems, we propose
algorithms to check the presence of the controllability properties. In
particular, we show that in such a setting dealing simultaneously with
preferences and uncertainty does not increase the complexity of controllability
testing. We also develop a dynamic execution algorithm, of polynomial
complexity, that produces temporal plans under uncertainty that are optimal
with respect to fuzzy preferences
Optimising Flexibility of Temporal Problems with Uncertainty
Temporal networks have been applied in many autonomous systems.
In real situations, we cannot ignore the uncertain factors when
using those autonomous systems. Achieving robust schedules and
temporal plans by optimising flexibility to tackle the
uncertainty is the motivation of the thesis.
This thesis focuses on the optimisation problems of temporal
networks with uncertainty and controllable options in the field
of Artificial Intelligence Planning and Scheduling. The goal of
this thesis is to construct flexibility and robustness metrics
for temporal networks under the constraints of different levels
of controllability. Furthermore, optimising flexibility for
temporal plans and schedules to achieve robust solutions with
flexible executions.
When solving temporal problems with uncertainty, postponing
decisions according to the observations of uncertain events
enables flexible strategies as the solutions instead of fixed
schedules or plans. Among the three levels of controllability of
the Simple Temporal Problem with Uncertainty (STPU), a problem is
dynamically controllable if there is a successful dynamic
strategy such that every decision in it is made according to the
observations of past events.
In the thesis, we make the following contributions. (1) We
introduce an optimisation model for STPU based on the existing
dynamic controllability checking algorithms. Some flexibility and
robustness measures are introduced based on the model. (2) We
extend the definition and verification algorithm of dynamic
controllability to temporal problems with controllable discrete
variables and uncertainty, which is called Controllable
Conditional Temporal Problems with Uncertainty (CCTPU). An
entirely dynamically controllable strategy of CCTPU consists of
both temporal scheduling and variable assignments being
dynamically decided, which maximize the flexibility of the
execution. (3) We introduce optimisation models of CCTPU under
fully dynamic controllability. The optimisation models aim to
answer the questions how flexible, robust or controllable a
schedule or temporal plan is. The experiments show that making
decisions dynamically can achieve better objective values than
doing statically.
The thesis also contributes to the field of AI planning and
scheduling by introducing robustness metrics of temporal
networks, proposing an envelope-based algorithm that can check
dynamic controllability of temporal networks with uncertainty and
controllable discrete decisions, evaluating improvements from
making decisions strongly controllable to temporally dynamically
controllable and fully dynamically controllable and comparing the
runtime of different implementations to present the scalability
of dynamically controllable strategies
Dynamic Controllability of Temporally-flexible Reactive Programs
In this paper we extend dynamic controllability of temporally-flexible plans to temporally-flexible reactive programs. We consider three reactive programming language constructs whose behavior depends on runtime observations; conditional execution, iteration, and exception handling. Temporally-flexible reactive programs are distinguished from temporally-flexible plans in that program execution is conditioned on the runtime state of the world. In addition, exceptions are thrown and caught at runtime in response to violated timing constraints, and handled exceptions are considered successful program executions. Dynamic controllability corresponds to a guarantee that a program will execute to completion, despite runtime constraint violations and uncertainty in runtime state. An algorithm is developed which frames the dynamic controllability problem as an AND/OR search tree over possible program executions. A key advantage of this approach is the ability to enumerate only a subset of possible program executions that guarantees dynamic controllability, framed as an AND/OR solution subtree
Some results and challenges Extending Dynamic Controllability to Agile Controllability in Simple Temporal Networks with Uncertainties
Simple Temporal Networks with Uncertainty (STNU) are an expressive means to represent temporal constraints, requirements, or obligations. They feature contingent timepoints, which are set by the environment with a specified interval. Dynamic controllability is the current most relaxed notion for checking that the constraints are not in conflict. It requires that a timepoint may only depend on earlier timepoints. Agile controllability extends dynamic controllability by taking into account that a later timepoint might already be known earlier and allowing a timepoint to depend on all timepoints whose value is known before. In this report, we formally introduce the notion of an STNU with oracle timepoints, formally define the notion of agile controllability, and discuss approaches for checking agile controllability
Extending Conditional Simple Temporal Networks with Partially Shrinkable Uncertainty
The proper handling of temporal constraints is crucial in many domains. As a particular challenge, temporal constraints must be also handled when different specific situations happen (conditional constraints) and when some event occurrences can be only observed at run time (contingent constraints). In this paper we introduce Conditional Simple Temporal Networks with Partially Shrinkable Uncertainty (CSTNPSUs), in which contingent constraints are made more flexible (guarded constraints) and they are also specified as conditional constraints. It turns out that guarded constraints require the ability to reason on both kinds of constraints in a seamless way. In particular, we discuss CSTNPSU features through a motivating example and, then, we introduce the concept of controllability for such networks and the related sound checking algorithm
Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty
In temporal planning, many different temporal network formalisms are used to
model real world situations. Each of these formalisms has different features
which affect how easy it is to determine whether the underlying network of
temporal constraints is consistent. While many of the simpler models have been
well-studied from a computational complexity perspective, the algorithms
developed for advanced models which combine features have very loose complexity
bounds. In this paper, we provide tight completeness bounds for strong, weak,
and dynamic controllability checking of temporal networks that have conditions,
disjunctions, and temporal uncertainty. Our work exposes some of the subtle
differences between these different structures and, remarkably, establishes a
guarantee that all of these problems are computable in PSPACE
Simple Temporal Networks with Partially Shrinkable Uncertainty
The Simple Temporal Network with Uncertainty (STNU) model focuses on the representation and evaluation of temporal constraints on time-point variables (timepoints), of which some (i.e., contingent timepoints) cannot be assigned (i.e., executed by the system), but only be observed. Moreover, a temporal constraint is expressed as an admissible range of delays between two timepoints. Regarding the STNU model, it is interesting to determine whether it is possible to execute all the timepoints under the control of the system, while still satisfying all given constraints, no matter when the contingent timepoints happen within the given time ranges (controllability check). Existing approaches assume that the original contingent time range cannot be modified during execution. In real world, however, the allowed time range may change within certain boundaries, but cannot be completely shrunk. To represent such possibility more properly, we propose Simple Temporal Network with Partially Shrinkable Uncertainty (STNPSU) as an extension of STNU. In particular, STNPSUs allow representing a contingent range in a way that can be shrunk during run time as long as shrinking does not go beyond a given threshold. We further show that STNPSUs allow representing STNUs as a special case, while maintaining the same efficiency for both controllability checks and execution
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