PyReason: Software for Open World Temporal Logic (original) (raw)

NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results. CCS CONCEPTS • Computing methodologies → Semantic networks; Temporal reasoning.

Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks

IEEE Control Systems Letters, 2021

Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w-GSTL) formulas. For learning w-GSTL formulas, we introduce a flexible w-GSTL formula structure in which the user's preference can be applied in the inferred w-GSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible w-GSTL formula structure. We initially train a neural network to learn the w-GSTL operators, and then train a second neural network to learn the parameters in a flexible w-GSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.

Information-Guided Temporal Logic Inference with Prior Knowledge

2019 American Control Conference (ACC), 2019

This paper investigates the problem of inferring knowledge from data so that the inferred knowledge is interpretable and informative to humans who have prior knowledge. Given a dataset as a collection of system trajectories, we infer parametric linear temporal logic (pLTL) formulas that are informative and satisfied by the trajectories in the dataset with high probability. The informativeness of the inferred formula is measured by the information gain with respect to given prior knowledge represented by a prior probability distribution. We first present two algorithms to compute the information gain with a focus on two types of prior probability distributions: stationary probability distributions and probability distributions expressed by discrete time Markov chains. Then we provide a method to solve the inference problem for a subset of pLTL formulas with polynomial time complexity with respect to the number of Boolean connectives in the formula. We provide implementations of the proposed approach on explaining anomalous patterns, patterns changes and explaining the policies of Markov decision processes.

Parameterisation of Reasoning on Temporal Markov Logic Networks

arXiv (Cornell University), 2022

We aim at improving reasoning on inconsistent and uncertain data. We focus on knowledge-graph data, extended with time intervals to specify their validity, as regularly found in historical sciences. We propose principles on semantics for efficient Maximum A-Posteriori inference on the new Temporal Markov Logic Networks (TMLN) which extend the Markov Logic Networks (MLN) by uncertain temporal facts and rules. We examine total and partial temporal (in)consistency relations between sets of temporal formulae. Then we propose a new Temporal Parametric Semantics, which may combine several sub-functions, allowing to use different assessment strategies. Finally, we expose the constraints that semantics must respect to satisfy our principles.

The Role of Annotated Logics in Ai: A Review Paper

Journal of Computer Science and Cybernetics, 2021

Annotated Logics are a category of non-classical logics that have recently appeared from a historical point of view. They are a type of paraconsistent, paracomplete and non-alethic logic. With the rapid development of AI and Automation and Robotics, more and more theory and techniques were coined to support the various issues that the themes were presenting. This expository work explores how to deal directly with conflicts (contradictions) and paracompleteness directly, without extra-logical devices. Support is given by the paraconsistent annotated evidential logic Et. Some applications are discussed.

Temporal logic inference for classification and prediction from data

Proceedings of the 17th international conference on Hybrid systems: computation and control - HSCC '14, 2014

This paper presents an inference algorithm that can discover temporal logic properties of a system from data. Our algorithm operates on finite time system trajectories that are labeled according to whether or not they demonstrate some desirable system properties (e.g. "the car successfully stops before hitting an obstruction"). A temporal logic formula that can discriminate between the desirable behaviors and the undesirable ones is constructed. The formulae also indicate possible causes for each set of behaviors (e.g. "If the speed of the car is greater than 15 m/s within 0.5s of brake application, the obstruction will be struck") which can be used to tune designs or to perform on-line monitoring to ensure the desired behavior. We introduce reactive parameter signal temporal logic (rPSTL), a fragment of parameter signal temporal logic (PSTL) that is expressive enough to capture causal, spatial, and temporal relationships in data. We define a partial order over the set of rPSTL formulae that is based on language inclusion. This order enables a directed search over this set, i.e. given a candidate rPSTL formula that does not adequately match the observed data, we can automatically construct a formula that will fit the data at least as well. Two case studies, one involving a cattle herding scenario and one involving a stochastic hybrid gene circuit model, are presented to illustrate our approach.

Automated deduction in a graphical temporal logic

Journal of Applied Non-Classical Logics, 1996

Graphical Interval Logic is a modal logic for reasoning about time in which the basic modality is the interval. The logic differs from other logics in that it has a natural intuitive graphical representation that resembles the timing diagrams drawn by system designers. We have implemented an automated deduction system for Graphical Interval Logic that includes a graphical user interface and an automated theorem prover. The graphical user interface enables the user to create Graphical Interval Logic formulas and proofs on a workstation display, and the theorem prover checks the validity of deductions in the logic. In this paper we describe the logic, the automated deduction system, and an application to robotics.

Towards generalizable neuro-symbolic reasoners

Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedSymbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of --not necessarily easily obtained-- data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of...

Temporal reasoning in Timegraph I–II

Intelligence/sigart Bulletin, 1993

We describe two domain-independent temporal reasoning systems called TimeGraph I and II which can be used in AI-applications as tools for e ciently managing large sets of relations in the Point Algebra, in the Interval Algebra, and metric information such as absolute times and durations. Our representation of time is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. TimeGraph I was originally developed by Taugher, Schubert and Miller in the context of story comprehension. TimeGraph II provides useful extensions, including e cient algorithms for handing inequations, and relations expressing point-interval exclusion and interval disjointness. These extensions make the system much more expressive in the representation of qualitative information and suitable for a large class of applications.

Annotation-based deduction in temporal logic

Lecture Notes in Computer Science, 1994

This paper presents a deductive system for predicate temporal logic with induction. Representing temporal operators by rst-order expressions enables temporal deduction to use the already developed techniques of rst-order deduction. But when translating from temporal logic to rst-order logic is done indiscriminately, the ensuing quanti cations and comparisons of time expressions encumber formulas, hindering deduction. So in the deductive system presented here, translation occurs more carefully, via rei cation rules. These rules paraphrase selected temporal formulas as nontemporal rst-order formulas with time annotations. This time reication process suppresses quanti cations (the process is analogous to quanti er skolemization) and uses addition instead of complicated combinations of comparisons. Some ordering conditions on arithmetic expressions can arise, but such are handled automatically by a specialpurpose uni cation algorithm plus a decision procedure for Presburger arithmetic. This deductive system is relatively complete. Contents