367,565 research outputs found
Analysis and Transformation Tools for Constrained Horn Clause Verification
Several techniques and tools have been developed for verification of
properties expressed as Horn clauses with constraints over a background theory
(CHC). Current CHC verification tools implement intricate algorithms and are
often limited to certain subclasses of CHC problems. Our aim in this work is to
investigate the use of a combination of off-the-shelf techniques from the
literature in analysis and transformation of Constraint Logic Programs (CLPs)
to solve challenging CHC verification problems. We find that many problems can
be solved using a combination of tools based on well-known techniques from
abstract interpretation, semantics-preserving transformations, program
specialisation and query-answer transformations. This gives insights into the
design of automatic, more general CHC verification tools based on a library of
components.Comment: To appear in Theory and Practice of Logic Programming (TPLP
A Framework for Automated Correctness Checking of Biochemical Protocol Realizations on Digital Microfluidic Biochips
Recent advances in digital microfluidic (DMF) technologies offer a promising
platform for a wide variety of biochemical applications, such as DNA analysis,
automated drug discovery, and toxicity monitoring. For on-chip implementation
of complex bioassays, automated synthesis tools have been developed to meet the
design challenges. Currently, the synthesis tools pass through a number of
complex design steps to realize a given biochemical protocol on a target DMF
architecture. Thus, design errors can arise during the synthesis steps. Before
deploying a DMF biochip on a safety critical system, it is necessary to ensure
that the desired biochemical protocol has been correctly implemented, i.e., the
synthesized output (actuation sequences for the biochip) is free from any
design or realization errors. We propose a symbolic constraint-based analysis
framework for checking the correctness of a synthesized biochemical protocol
with respect to the original design specification. The verification scheme
based on this framework can detect several post-synthesis fluidic violations
and realization errors in 2D-array based or pin-constrained biochips as well as
in cyberphysical systems. It further generates diagnostic feedback for error
localization. We present experimental results on the polymerase chain reaction
(PCR) and in-vitro multiplexed bioassays to demonstrate the proposed
verification approach
Timing- and power-driven ALU design training using spreadsheet-based arithmetic exploration
We describe master-level design training that combines ALU design exercises based on commercial synthesis tools and arithmetic explorations based on spreadsheets. Despite its limited complexity, the ALU has a few important properties that make it suitable for our training; 1) the ALU subcircuits are diverse and contain both short and long timing paths, 2) timing-driven design is called for, since the ALU is a performance bottleneck, and 3) the ALU is continuously used, making power dissipation an important design parameter. After enforcing strict timing constraints during synthesis of the ALU, the students need to reconsider how to implement the arithmetic block, which initially is too slow. Here, performing arithmetic explorations inside an innovative spreadsheet environment helps to visualize circuit implementation tradeoffs. The final phase in the design training focuses on power analysis and demonstrates that the choice of timing constraint impacts power dissipation
Automated Design of Elevator Systems: Experimenting with Constraint-Based Approaches
System configuration and design is a well-established topic
in AI. While many successful applications exists, there are still areas of
manufacturing where AI techniques find little or no application. We focus
on one such area, namely building and installation of elevator systems,
for which we are developing an automated design and configuration tool.
The questions that we address in this paper are: (i) What are the best
ways to encode some subtasks of elevator design into constraint-based
representations? (ii) What are the best tools available to solve the encodings? We contribute an empirical analysis to address these questions
in our domain of interest, as well as the complete set of benchmarks to
foster further researc
Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems
Semidefinite programming is a powerful tool in the design and analysis of
approximation algorithms for combinatorial optimization problems. In
particular, the random hyperplane rounding method of Goemans and Williamson has
been extensively studied for more than two decades, resulting in various
extensions to the original technique and beautiful algorithms for a wide range
of applications. Despite the fact that this approach yields tight approximation
guarantees for some problems, e.g., Max-Cut, for many others, e.g., Max-SAT and
Max-DiCut, the tight approximation ratio is still unknown. One of the main
reasons for this is the fact that very few techniques for rounding semidefinite
relaxations are known.
In this work, we present a new general and simple method for rounding
semi-definite programs, based on Brownian motion. Our approach is inspired by
recent results in algorithmic discrepancy theory. We develop and present tools
for analyzing our new rounding algorithms, utilizing mathematical machinery
from the theory of Brownian motion, complex analysis, and partial differential
equations. Focusing on constraint satisfaction problems, we apply our method to
several classical problems, including Max-Cut, Max-2SAT, and MaxDiCut, and
derive new algorithms that are competitive with the best known results. To
illustrate the versatility and general applicability of our approach, we give
new approximation algorithms for the Max-Cut problem with side constraints that
crucially utilizes measure concentration results for the Sticky Brownian
Motion, a feature missing from hyperplane rounding and its generalization
Tracking-based distributed equilibrium seeking for aggregative games
We propose fully-distributed algorithms for Nash equilibrium seeking in
aggregative games over networks. We first consider the case where local
constraints are present and we design an algorithm combining, for each agent,
(i) the projected pseudo-gradient descent and (ii) a tracking mechanism to
locally reconstruct the aggregative variable. To handle coupling constraints
arising in generalized settings, we propose another distributed algorithm based
on (i) a recently emerged augmented primal-dual scheme and (ii) two tracking
mechanisms to reconstruct, for each agent, both the aggregative variable and
the coupling constraint satisfaction. Leveraging tools from singular
perturbations analysis, we prove linear convergence to the Nash equilibrium for
both schemes. Finally, we run extensive numerical simulations to confirm the
effectiveness of our methods and compare them with state-of-the-art distributed
equilibrium-seeking algorithms
Tracking-based distributed equilibrium seeking for aggregative games
We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we design an algorithm combining, for each agent, (i) the projected pseudo-gradient descent and (ii) a tracking mechanism to locally reconstruct the aggregative variable. To handle coupling constraints arising in generalized settings, we propose another distributed algorithm based on (i) a recently emerged augmented primal-dual scheme and (ii) two tracking mechanisms to reconstruct, for each agent, both the aggregative variable and the coupling constraint satisfaction. Leveraging tools from singular perturbations analysis, we prove linear convergence to the Nash equilibrium for both schemes. Finally, we run extensive numerical simulations to confirm the effectiveness of our methods and compare them with state-of-the-art distributed equilibrium-seeking algorithms
Design optimisation of complex space systems under epistemic uncertainty
This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model.This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model
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