200 research outputs found

### Now or never: negotiating efficiently with unknown counterparts

We define a new protocol rule, Now or Never (NoN), for bilateral negotiation processes which allows self-motivated competitive agents to efficiently carry out multi-variable negotiations with remote untrusted parties, where privacy is a major concern and agents know nothing about their opponent. By building on the geometric concepts of convexity and convex hull, NoN ensures a continuous progress of the negotiation, thus neutralising malicious or inefficient opponents. In par- ticular, NoN allows an agent to derive in a finite number of steps, and independently of the behaviour of the opponent, that there is no hope to find an agreement. To be able to make such an inference, the interested agent may rely on herself only, still keeping the highest freedom in the choice of her strategy.
We also propose an actual NoN-compliant strategy for an automated agent and evaluate the computational feasibility of the overall approach on instances of practical size

### Experimental evaluation of algorithms forsolving problems with combinatorial explosion

Solving problems with combinatorial explosionplays an important role in decision-making, sincefeasible or optimal decisions often depend on anon-trivial combination of various factors. Gener-ally, an effective strategy for solving such problemsis merging different viewpoints adopted in differ-ent communities that try to solve similar prob-lems; such that algorithms developed in one re-search area are applicable to other problems, orcan be hybridised with techniques in other ar-eas. This is one of the aims of the RCRA (Ra-gionamento Automatico e Rappresentazione dellaConoscenza) group,1the interest group of the Ital-ian Association for Artificial Intelligence (AI*IA)on knowledge representation and automated rea-soning, which organises its annual meetings since1994

### Parallelization of cycle-based logic simulation

Verification of digital circuits by Cycle-based simulation can be performed in parallel. The parallel implementation requires two phases: the compilation phase, that sets up the data needed for the
execution of the simulation, and the simulation phase, that consists in executing the parallel simulation of the considered circuit for a certain number of cycles. During the early phase of design, compilation phase has to be repeated each time a bug is found. Thus, if the time of the compilation phase is too high, the advantages stemming from the parallel approach may be lost. In this work we propose an
effective version of the compilation phase and compute the corresponding execution time. We also analyze the percentage of execution time required by the different steps of the compilation phase for
a set of literature benchmarks. Further, we implemented the simulation phase exploiting the GPU architecture, and we computed the execution times for a set of benchmarks obtaining values comparable
with literature ones. Finally, we implemented the sequential version of the Cycle-based simulation in such a way that the execution time is optimized. We used the sequential values to compute the speedup
of the parallel version for the considered set of benchmarks

### Generalizing Consistency and other Constraint Properties to Quantified Constraints

Quantified constraints and Quantified Boolean Formulae are typically much
more difficult to reason with than classical constraints, because quantifier
alternation makes the usual notion of solution inappropriate. As a consequence,
basic properties of Constraint Satisfaction Problems (CSP), such as consistency
or substitutability, are not completely understood in the quantified case.
These properties are important because they are the basis of most of the
reasoning methods used to solve classical (existentially quantified)
constraints, and one would like to benefit from similar reasoning methods in
the resolution of quantified constraints. In this paper, we show that most of
the properties that are used by solvers for CSP can be generalized to
quantified CSP. This requires a re-thinking of a number of basic concepts; in
particular, we propose a notion of outcome that generalizes the classical
notion of solution and on which all definitions are based. We propose a
systematic study of the relations which hold between these properties, as well
as complexity results regarding the decision of these properties. Finally, and
since these problems are typically intractable, we generalize the approach used
in CSP and propose weaker, easier to check notions based on locality, which
allow to detect these properties incompletely but in polynomial time

### Residential demand management using individualised demand aware price policies

This paper presents a novel approach to Demand Side Management (DSM), using an â€śindividualisedâ€ť price policy, where each end user receives a separate electricity pricing scheme designed to incentivise demand management in order to optimally manage flexible demands. These pricing schemes have the objective of reducing the peaks in overall system demand in such a way that the average electricity price each individual user receives is non-discriminatory. It is shown in the paper that this approach has a number of advantages and benefits compared to traditional DSM approaches. The â€śdemand aware price policyâ€ť approach outlined in this paper exploits the knowledge, or demand-awareness, obtained from advanced metering infrastructure. The presented analysis includes a detailed case study of an existing European distribution network where DSM trial data was available from the residential end-users

### Anytime system level verification via parallel random exhaustive hardware in the loop simulation

System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems.
We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability).
We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability

### Simulator Semantics for System Level Formal Verification

Many simulation based Bounded Model Checking approaches to System Level
Formal Verification (SLFV) have been devised. Typically such approaches exploit
the capability of simulators to save computation time by saving and restoring
the state of the system under simulation. However, even though such approaches
aim to (bounded) formal verification, as a matter of fact, the simulator
behaviour is not formally modelled and the proof of correctness of the proposed
approaches basically relies on the intuitive notion of simulator behaviour.
This gap makes it hard to check if the optimisations introduced to speed up the
simulation do not actually omit checking relevant behaviours of the system
under verification.
The aim of this paper is to fill the above gap by presenting a formal
semantics for simulators.Comment: In Proceedings GandALF 2015, arXiv:1509.0685

### Combining Relational Algebra, SQL, Constraint Modelling, and Local Search

The goal of this paper is to provide a strong integration between constraint
modelling and relational DBMSs. To this end we propose extensions of standard
query languages such as relational algebra and SQL, by adding constraint
modelling capabilities to them. In particular, we propose non-deterministic
extensions of both languages, which are specially suited for combinatorial
problems. Non-determinism is introduced by means of a guessing operator, which
declares a set of relations to have an arbitrary extension. This new operator
results in languages with higher expressive power, able to express all problems
in the complexity class NP. Some syntactical restrictions which make data
complexity polynomial are shown. The effectiveness of both extensions is
demonstrated by means of several examples. The current implementation, written
in Java using local search techniques, is described. To appear in Theory and
Practice of Logic Programming (TPLP)Comment: 30 pages, 5 figure

### On minimising the maximum expected verification time

Cyber Physical Systems (CPSs) consist of hardware and software components. To verify that the whole (i.e., software + hardware) system meets the given specifications, exhaustive simulation-based approaches (Hardware In the Loop Simulation, HILS) can be effectively used by first generating all relevant simulation scenarios (i.e., sequences of disturbances) and then actually simulating all of them (verification phase). When considering the whole verification activity, we see that the above mentioned verification phase is repeated until no error is found. Accordingly, in order to minimise the time taken by the whole verification activity, in each verification phase we should, ideally, start by simulating scenarios witnessing errors (counterexamples). Of course, to know beforehand the set of such scenarios is not feasible. In this paper we show how to select scenarios so as to minimise the Worst Case Expected Verification Tim

### Optimising Highly-Parallel Simulation-Based Verification of Cyber-Physical Systems

Cyber-Physical Systems (CPSs), comprising both software and physical
components, arise in many industry-relevant domains and are often mission- or
safety-critical.
System-Level Verification (SLV) of CPSs aims at certifying that given (e.g.,
safety or liveness) specifications are met, or at estimating the value of some
KPIs, when the system runs in its operational environment, i.e., in presence of
inputs (from users or other systems) and/or of additional, uncontrolled
disturbances.
To enable SLV of complex systems from the early design phases, the currently
most adopted approach envisions the simulation of a system model under the
(time bounded) operational scenarios of interest. Simulation-based SLV can be
computationally prohibitive (years of sequential simulation), since model
simulation is computationally intensive and the set of scenarios of interest
can huge.
We present a technique that, given a collection of scenarios of interest
(extracted from mass-storage databases or from symbolic structures, e.g.,
constraint-based scenario generators), computes parallel shortest simulation
campaigns, which drive a possibly large number of system model simulators
running in parallel in a HPC infrastructure through all (and only) those
scenarios in the user-defined (possibly random) order, by wisely avoiding
multiple simulations of repeated trajectories, thus minimising the overall
completion time, compatibly with the available simulator memory capacity.
Our experiments on Modelica/FMU and Simulink case study models with up to
~200 million scenarios show that our optimisation yields speedups as high as
8x. This, together with the enabled massive parallelisation, makes practically
viable (a few weeks in a HPC infrastructure) verification tasks (both
statistical and exhaustive, with respect to the given set of scenarios) which
would otherwise take inconceivably long time

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