128 research outputs found
Structural Symmetries for Fully Observable Nondeterministic Planning
Symmetry reduction has significantly contributed to the success of classical planning as heuristic search. However, it is an open question if symmetry reduction techniques can be lifted to fully observable nondeterministic (FOND) planning. We generalize the concepts of structural symmetries and symmetry reduction to FOND planning and specifically to the LAO* algorithm. Our base implementation of LAO* in the Fast Downward planner is competitive with the LAO*-based FOND planner myND. Our experiments further show that symmetry reduction can yield strong performance gains compared to our base implementation of LAO*
Stubborn Sets for Fully Observable Nondeterministic Planning
Pruning techniques based on strong stubborn sets have recently shown their potential for SAS+ planning as heuristic search. Strong stubborn sets exploit operator independency to safely prune the search space. Like SAS
+ planning, fully observable nondeterministic (FOND) planning faces the state explosion problem. However, it is unclear how stubborn set techniques carry over to the nondeterministic setting. In this paper, we introduce stubborn set pruning to FOND planning. We lift the notion of strong stubborn sets and introduce the conceptually more powerful notion of weak stubborn sets to FOND planning. Our experimental analysis shows that weak stubborn sets are beneficial to an LAO* search, and in particular show favorable performance when combined with symmetries and active operator pruning
Strengthening Canonical Pattern Databases with Structural Symmetries
Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks
We transfer the notion of structural symmetries to lifted planning task representations, based on abstract structures which we define to model planning tasks. We show that symmetries are preserved by common grounding methods and we shed some light on the relation to previous symmetry concepts used in planning. Using a suitable graph representation of lifted tasks, our experimental analysis of common planning benchmarks reveals that symmetries occur in the lifted representation of many domains. Our work establishes the theoretical ground for exploiting symmetries beyond their previous scope, such as for faster grounding and mutex generation, as well as for state space transformations and reduction
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
Quantitative Analysis of Information Leakage in Probabilistic and Nondeterministic Systems
This thesis addresses the foundational aspects of formal methods for
applications in security and in particular in anonymity. More concretely, we
develop frameworks for the specification of anonymity properties and propose
algorithms for their verification. Since in practice anonymity protocols always
leak some information, we focus on quantitative properties, which capture the
amount of information leaked by a protocol.
The main contribution of this thesis is cpCTL, the first temporal logic that
allows for the specification and verification of conditional probabilities
(which are the key ingredient of most anonymity properties). In addition, we
have considered several prominent definitions of information-leakage and
developed the first algorithms allowing us to compute (and even approximate)
the information leakage of anonymity protocols according to these definitions.
We have also studied a well-known problem in the specification and analysis of
distributed anonymity protocols, namely full-information scheduling. To
overcome this problem, we have proposed an alternative notion of scheduling and
adjusted accordingly several anonymity properties from the literature. Our last
major contribution is a debugging technique that helps on the detection of
flaws in security protocols.Comment: thesis, ISBN: 978-94-91211-74-
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