ScalaSMT: satisfiability modulo theory in Scala (tool paper)

Author(s):  
Franck Cassez ◽  
Anthony M. Sloane
2021 ◽  
Author(s):  
Vie’an Huzair Majalawa ◽  
Putranto Hadi Utomo ◽  
Tri Atmojo Kusmayadi ◽  
Diari Indriati

2020 ◽  
Vol 5 (47) ◽  
pp. eabc3000
Author(s):  
Kunal Shah ◽  
Grant Ballard ◽  
Annie Schmidt ◽  
Mac Schwager

Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adélie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response.


2012 ◽  
Vol 21 (06) ◽  
pp. 1250025 ◽  
Author(s):  
FLORIAN LETOMBE ◽  
JOAO MARQUES-SILVA

Boolean Satisfiability (SAT) solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. SAT solvers are also the building block of more sophisticated decision procedures, including Satisfiability Modulo Theory (SMT) solvers. The large number of applications of SAT yields ever more challenging problem instances, and motivate the development of more efficient algorithms. Recent work studied hybrid approaches for SAT, which involves integrating incomplete and complete SAT solvers. This paper proposes a number of improvements to hybrid SAT solvers. Experimental results demonstrate that the proposed optimizations are effective. The resulting algorithms in general perform better and, more importantly, are significantly more robust.


2016 ◽  
Vol 292 ◽  
pp. 32-48 ◽  
Author(s):  
Carlos Ansótegui ◽  
Miquel Bofill ◽  
Felip Manyà ◽  
Mateu Villaret

Author(s):  
Nicolás Gálvez Ramírez ◽  
Youssef Hamadi ◽  
Eric Monfroy ◽  
Frédéric Saubion

Author(s):  
Alexander Diedrich ◽  
Alexander Maier ◽  
Oliver Niggemann

Currently, detecting and isolating faults in hybrid systems is often done manually with the help of human operators. In this paper we present a novel model-based diagnosis approach for automatically diagnosing hybrid systems. The approach has two parts: First, modelling dynamic system behaviour is done through well-known state space models using differential equations. Second, from the state space models we calculate Boolean residuals through an observer-pattern. The novelty lies in implementing the observer pattern through the use of a symbolic system description specified in satisfiability theory modulo linear arithmetic. With this, we create a static situation for the diagnosis algorithm and decouple modelling and diagnosis. Evaluating the system description generates one Boolean residual for each component. These residuals constitute the fault symptoms. To find the minimum cardinality diagnosis from these symptoms we employ Reiter’s diagnosis lattice.For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. We show that the presented approach is able to identify all injected faults.


Author(s):  
Alexander Diedrich ◽  
Kaja Balzereit ◽  
Oliver Niggemann

AbstractMaintaining modern production machinery requires a significant amount of time and money. Still, plants suffer from expensive production stops and downtime due to faults within individual components. Often, plants are too complex and generate too much data to make manual analysis and diagnosis feasible. Instead, faults often occur unnoticed, resulting in a production stop. It is then the task of highly-skilled engineers to recognise and analyse symptoms and devise a diagnosis. Modern algorithms are more effective and help to detect and isolate faults faster and more precise, thus leading to increased plant availability and lower operating costs.In this paper we attempt to solve some of the described challenges. We describe a concept for an automated framework for hybrid cyberphysical production systems performing two distinct tasks: 1) fault diagnosis and 2) reconfiguration. For diagnosis, the inputs are connection and behaviour models of the components contained within the system and a model describing their causal dependencies. From this information the framework is able to automatically derive a diagnosis provided a set of known symptoms. Taking the output of the diagnosis as a foundation, the reconfiguration part generates a new configuration, which, if applicable, automatically recovers the plant from its faulty state and resumes production. The described concept is based on predicate logic, specifically Satisfiability-Modulo-Theory. The input models are transformed into logical predicates. These predicates are the input to an implementation of Reiter’s diagnosis algorithm, which identifies the minimum-cardinality diagnosis. Taking this diagnosis, a reconfiguration algorithm determines a possible, alternative control, if existing. Therefore the current system structure described by the connection and component models is analysed and alternative production plans are searched. If such an alternative plan exists, it is transmitted to the control of the system. Otherwise, an error that the system is not reconfigurable is returned.


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