A Novel SAT-Based Approach to Model Based Diagnosis

Metodi, Amit; Stern, Roni; Kalech, Meir; Codish, Michael

doi:10.1613/jair.4503

Cited by 46 publications

(58 citation statements)

References 33 publications

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“…These methods guarantee sound diagnoses (i.e., they return only valid diagnoses), and some of them are even complete (i.e., all diagnoses are returned). However, they tend to fail for large systems due to infeasible runtime or space requirements [5].…”

Section: Sdmentioning

confidence: 99%

“…The limitation of this approach is that there is no guarantee that the size of the compiled representation will not be exponential in the number of system components. Another approach is SATbD, a compilation-based MBD algorithm that compiles MBD into Boolean satisfiability problem (SAT) [5,67], and then uses state-of-the-art SAT solvers to find the possible diagnoses. We follow a similar line of work here, but instead of a classical SAT solver we use a conflict-directed algorithm, which allows us to find conflicts in polynomial time in our domain by using a Logic-based Truth Maintaining System [68].…”

Section: Sdmentioning

confidence: 99%

“…The first step to create such a tool is a compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem. Our approach is based on modeling the physiotherapy diagnosis process as a model-based diagnosis (MBD) problem [2][3][4][5]. MBD relies on a model of the diagnosed system, which is utilized to simulate the expected behaviour of the system given the operational context (typically, the system inputs).…”

Section: Introductionmentioning

confidence: 99%

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“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

et al. 2020

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Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

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Section: Sdmentioning

confidence: 99%

Section: Sdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

et al. 2020

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“…Given the vector C as COMPS and the system description SD it is possible to employ any diagnosis algorithm such as CDA* (Williams and Ragno 2007), GDE (Reiter 1987), or SATbD (Metodi et al 2014) to find minimum cardinality diagnoses. Here we have used Reiter's diagnosis lattice.…”

Section: Definition 4 (Satisfiability Modulo Linear Arithmetic)mentioning

confidence: 99%

Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory

Diedrich

Maier

Niggemann³

2019

AAAI

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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.

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“…This is computationally more difficult than finding a single diagnosis, but also more realistic from the perspective of applications. Again, state-of-the-art performance in the weak fault model is achieved using a SAT-solver [33]. The hardware's natural ability to rapidly generate low-energy samples lends itself to applications in which a diverse set of optimal solutions are required.…”

Section: Generating Diverse Solutionsmentioning

confidence: 99%

Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis

et al. 2016

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Current quantum annealing (QA) hardware suffers from practical limitations such as finite temperature, sparse connectivity, small qubit numbers, and control error. We propose new algorithms for mapping boolean constraint satisfaction problems (CSPs) onto QA hardware mitigating these limitations. In particular we develop a new embedding algorithm for mapping a CSP onto a hardware Ising model with a fixed sparse set of interactions, and propose two new decomposition algorithms for solving problems too large to map directly into hardware.The mapping technique is locally-structured, as hardware compatible Ising models are generated for each problem constraint, and variables appearing in different constraints are chained together using ferromagnetic couplings. In contrast, global embedding techniques generate a hardware independent Ising model for all the constraints, and then use a minor-embedding algorithm to generate a hardware compatible Ising model. We give an example of a class of CSPs for which the scaling performance of D-Wave's QA hardware using the local mapping technique is significantly better than global embedding.We validate the approach by applying D-Wave's hardware to circuit-based fault-diagnosis. For circuits that embed directly, we find that the hardware is typically able to find all solutions from a min-fault diagnosis set of size N using 1000N samples, using an annealing rate that is 25 times faster than a leading SAT-based sampling method. Further, we apply decomposition algorithms to find min-cardinality faults for circuits that are up to 5 times larger than can be solved directly on current hardware. arXiv:1603.03111v1 [quant-ph]

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A Novel SAT-Based Approach to Model Based Diagnosis

Cited by 46 publications

References 33 publications

“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory

Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis

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