Learning invariants using decision trees and implication counterexamples

Garg, Pranav; Neider, Daniel; Madhusudan, P.; Roth, Dan

doi:10.1145/2914770.2837664

Cited by 60 publications

(51 citation statements)

References 58 publications

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“…Data pick up is itself judged by measure called entropy , which utilized to measures the impurity of set of training objects. For a collection of data set S, entropy formula is given in equation 1, formulated by Claude Shannon , also used by [28,[35][36][37][38]. Entropy helps decision tree to determine how informative a node is.…”

Section: Algorithm 2: Id3 Working Algorithm Of Decision Treementioning

confidence: 99%

An IoT-Based Intelligent Wound Monitoring System

et al. 2019

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Clinical research of wound assessment focused on physical appearance of wound i.e. wound width, shape, color etc. Although, wound appearance is most crucial factors to influence healing process. however, apart from wound appearance other factors also contribute in healing process. Wound internal and external environment is one such factor that may show positive or negative impact on healing. Internet of things extensively popular during last decade, due to its heavy applications in almost all domains i.e. agriculture, health, marketing, banking, home etc. Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status of wound assessment by categorization of assessment results in one of three class i.e. good, satisfactory or alarming. We implemented decision tree in MATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree. The efficient feature split of decision tree improved training accuracy rate and performance of decision tree.

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Section: Algorithm 2: Id3 Working Algorithm Of Decision Treementioning

confidence: 99%

An IoT-Based Intelligent Wound Monitoring System

et al. 2019

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“…We evaluated FreqHorn-2 on various safe and buggy programs taken from SVCOMP 3 and literature (e.g., [9,15]). Since most of benchmarks, proposed by [9], appeared to be solvable during the bootstrapping of FreqHorn-2 (more details in Sect.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, k-induction was benefitted from lemmas obtained from PDR [21]. A promising idea to exploit the data from traces [12,15] while creating and manipulating the candidates for invariants could also be used in our syntax-guided approach: at least we could add more constants to the grammar. However we are currently unaware of a strategy to find meaningful constants and to avoid over-population of the grammar by too many constants.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Syntax-Guided Invariant Synthesis

Fedyukovich

Bodík

2018

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We present a fast algorithm for syntax-guided synthesis of inductive invariants which combines enumerative learning with inductive-subset extraction, leverages counterexamples-to-induction and interpolation-based bounded proofs. It is a variant of a recently proposed probabilistic method, called FreqHorn, which is however less dependent on heuristics than its predecessor. We present an evaluation of the new algorithm on a large set of benchmarks and show that it exhibits a more predictable behavior than its predecessor, and it is competitive to the state-of-the-art invariant synthesizers based on Property Directed Reachability.

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“…During construction of decision trees, the predicates for the inner nodes are chosen based on a supplied metric, which heuristicly attempts to select predicates leading into small trees. The entropy-based information gain is the most prevalent metric to construct decision trees, in machine learning [40,46] as well as in formal meth-ods [3,9,27,42]. Algorithm 2 presents a split procedure utilizing information gain, supplemented with a stand-in metric proposed in [11].…”

Section: Splitting Criterion For Small Decision Trees With Classifiersmentioning

confidence: 99%

Strategy Representation by Decision Trees with Linear Classifiers

Ashok

Brázdil

Chatterjee

et al. 2019

Quantitative Evaluation of Systems

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Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of ω-regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategies, and we consider succinct representation of such strategies. The decision-tree data structure from machine learning retains the flavor of decisions of strategies and allows entropy-based minimization to obtain succinct trees. However, in contrast to traditional machine-learning problems where small errors are allowed, for winning strategies in graph games and MDPs no error is allowed, and the decision tree must represent the entire strategy. In this work we propose decision trees with linear classifiers for representation of strategies in graph games and MDPs. We have implemented strategy representation using this data structure and we present experimental results for problems on graph games and MDPs, which show that this new data structure presents a much more efficient strategy representation as compared to standard decision trees.

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Learning invariants using decision trees and implication counterexamples

Cited by 60 publications

References 58 publications

An IoT-Based Intelligent Wound Monitoring System

An IoT-Based Intelligent Wound Monitoring System

Accelerating Syntax-Guided Invariant Synthesis

Strategy Representation by Decision Trees with Linear Classifiers

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