4,039 research outputs found
Probabilistic Model Checking for Energy Analysis in Software Product Lines
In a software product line (SPL), a collection of software products is
defined by their commonalities in terms of features rather than explicitly
specifying all products one-by-one. Several verification techniques were
adapted to establish temporal properties of SPLs. Symbolic and family-based
model checking have been proven to be successful for tackling the combinatorial
blow-up arising when reasoning about several feature combinations. However,
most formal verification approaches for SPLs presented in the literature focus
on the static SPLs, where the features of a product are fixed and cannot be
changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt
feature combinations of a product dynamically after deployment. The main
contribution of the paper is a compositional modeling framework for dynamic
SPLs, which supports probabilistic and nondeterministic choices and allows for
quantitative analysis. We specify the feature changes during runtime within an
automata-based coordination component, enabling to reason over strategies how
to trigger dynamic feature changes for optimizing various quantitative
objectives, e.g., energy or monetary costs and reliability. For our framework
there is a natural and conceptually simple translation into the input language
of the prominent probabilistic model checker PRISM. This facilitates the
application of PRISM's powerful symbolic engine to the operational behavior of
dynamic SPLs and their family-based analysis against various quantitative
queries. We demonstrate feasibility of our approach by a case study issuing an
energy-aware bonding network device.Comment: 14 pages, 11 figure
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
Verifying and Synthesising Multi-Agent Systems against One-Goal Strategy Logic Specifications
© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaa1.org). All rights reserved.Strategy Logic (SL) has recently come to the fore as a useful specification language to reason about multi-agent systems. Its one-goal fragment, or SL[1g], is of particular interest as it strictly subsumes widely used logics such as ATL∗, while maintaining attractive complexity features. In this paper we put forward an automata-based methodology for verifying and synthesising multi-agent systems against specifications given in SL[Ig], We show that the algorithm is sound and optimal from a computational point of view. A key feature of the approach is that all data structures and operations on them can be performed on BDDs. We report on a BDD-based model checker implementing the algorithm and evaluate its performance on the fair process scheduler synthesis
Determinacy in Discrete-Bidding Infinite-Duration Games
In two-player games on graphs, the players move a token through a graph to
produce an infinite path, which determines the winner of the game. Such games
are central in formal methods since they model the interaction between a
non-terminating system and its environment. In bidding games the players bid
for the right to move the token: in each round, the players simultaneously
submit bids, and the higher bidder moves the token and pays the other player.
Bidding games are known to have a clean and elegant mathematical structure that
relies on the ability of the players to submit arbitrarily small bids. Many
applications, however, require a fixed granularity for the bids, which can
represent, for example, the monetary value expressed in cents. We study, for
the first time, the combination of discrete-bidding and infinite-duration
games. Our most important result proves that these games form a large
determined subclass of concurrent games, where determinacy is the strong
property that there always exists exactly one player who can guarantee winning
the game. In particular, we show that, in contrast to non-discrete bidding
games, the mechanism with which tied bids are resolved plays an important role
in discrete-bidding games. We study several natural tie-breaking mechanisms and
show that, while some do not admit determinacy, most natural mechanisms imply
determinacy for every pair of initial budgets
Dynamic resource allocation games
In resource allocation games, selfish players share resources that are needed in order to fulfill their objectives. The cost of using a resource depends on the load on it. In the traditional setting, the players make their choices concurrently and in one-shot. That is, a strategy for a player is a subset of the resources. We introduce and study dynamic resource allocation games. In this setting, the game proceeds in phases. In each phase each player chooses one resource. A scheduler dictates the order in which the players proceed in a phase, possibly scheduling several players to proceed concurrently. The game ends when each player has collected a set of resources that fulfills his objective. The cost for each player then depends on this set as well as on the load on the resources in it – we consider both congestion and cost-sharing games. We argue that the dynamic setting is the suitable setting for many applications in practice. We study the stability of dynamic resource allocation games, where the appropriate notion of stability is that of subgame perfect equilibrium, study the inefficiency incurred due to selfish behavior, and also study problems that are particular to the dynamic setting, like constraints on the order in which resources can be chosen or the problem of finding a scheduler that achieves stability
- …