83 research outputs found
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
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
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
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
Reasoning and querying bounds on differences with layered preferences
Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and
calories. Recently, some approaches have extended the
BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction,
considering probabilities or preferences. While previous
approaches have mainly aimed at providing an
optimal solution to the set of constraints, we propose
an innovative class of approaches in which constraint
propagation algorithms aim at identifying the \u201cspace of
solutions\u201d (i.e., the minimal network) with their preferences,
and query answering mechanisms are provided
to explore the space of solutions as required, for
example, in decision support tasks. Aiming at generality,
we propose a class of approaches parametrized
over user\u2010defined scales of qualitative preferences (e.g.,
Low, Medium, High, and Very High), utilizing the resume
and extension operations to combine preferences,
and considering different formalisms to associate preferences
with BoDs. We consider both \u201cgeneral\u201d preferences
and a form of layered preferences that we call
\u201cpyramid\u201d preferences. The properties of the class of
approaches are also analyzed. In particular, we show
that, when the resume and extension operations are
defined such that they constitute a closed semiring, a
more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation
of the constraint propagation algorithms
Consistency checking of STNs with decisions: Managing temporal and access-control constraints in a seamless way
A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN to model temporal plans with decisions. STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. In this paper, we deal with the use of STNDs for modeling and synthesizing execution strategies. We propose an incremental hybrid SAT-based consistency checking algorithm for STNDs that is faster than the one previously proposed and allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). We carry out an experimental evaluation with Kappa, a tool that we developed for STNDs. We also show that any STND can be easily translated into a disjunctive temporal network and vice versa
Conditional Simple Temporal Networks with Uncertainty and Decisions
A conditional simple temporal network with uncertainty (CSTNU) is a framework able to model temporal plans subject to both conditional constraints and uncertain durations. The combination of these two characteristics represents the uncontrollable part of the network. That is, before the network starts executing, we do not know completely which time points and constraints will be taken into consideration nor how long the uncertain durations will last. Dynamic controllability (DC) implies the existence of a strategy scheduling the time points of the network in real time depending on how the uncontrollable part behaves. Despite all this, CSTNUs fail to model temporal plans in which a few conditional constraints are under control and may therefore influence (or be influenced by) the uncontrollable part. To bridge this gap, this paper proposes conditional simple temporal networks with uncertainty and decisions (CSTNUDs) which introduce decision time points into the specification in order to operate on this conditional part under control. We model the dynamic controllability checking (DC-checking) of a CSTNUD as a two-player game in which each player makes his moves in his turn at a specific time instant. We give an encoding into timed game automata for a sound and complete DC-checking. We also synthesize memoryless execution strategies for CSTNUDs proved to be DC. The proposed approach is fully automated
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
This short review aims to make the reader familiar with state-of-the-art
works relating to planning, scheduling and learning. First, we study
state-of-the-art planning algorithms. We give a brief introduction of neural
networks. Then we explore in more detail graph neural networks, a recent
variant of neural networks suited for processing graph-structured inputs. We
describe briefly the concept of reinforcement learning algorithms and some
approaches designed to date. Next, we study some successful approaches
combining neural networks for path-planning. Lastly, we focus on temporal
planning problems with uncertainty.Comment: AAAI-format & update
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