Past-present temporal programs over finite traces

• URL: http://arxiv.org/abs/2307.12620v2
• Date: Sat, 20 Jan 2024 14:14:12 GMT
• Title: Past-present temporal programs over finite traces
• Authors: Pedro Cabalar, Mart\'in Di\'eguez, Fran\c{c}ois Laferri\`ere, Torsten Schaub
• Abstract summary: We study the so-called past-present syntactic subclass, which consists of a set of logic programming rules whose body references to the past and head to the present. We extend the definitions of completion and loop formulas to the case of past-present formulas, which allows capturing the temporal stable models of a set of past-present temporal programs.
• Score: 1.4835015204811504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extensions of Answer Set Programming with language constructs from temporal logics, such as temporal equilibrium logic over finite traces (TELf), provide an expressive computational framework for modeling dynamic applications. In this paper, we study the so-called past-present syntactic subclass, which consists of a set of logic programming rules whose body references to the past and head to the present. Such restriction ensures that the past remains independent of the future, which is the case in most dynamic domains. We extend the definitions of completion and loop formulas to the case of past-present formulas, which allows capturing the temporal stable models of a set of past-present temporal programs by means of an LTLf expression.

Related papers

• Temporal Many-valued Conditional Logics: a Preliminary Report [1.228936170812787]
  We start from a many-valued logic with typicality, and extend it with the temporal operators of the Linear Time Temporal Logic. We also consider an instantiation of the formalism for gradual argumentation.
  arXiv  Detail & Related papers  (2024-09-06T16:23:31Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View [73.73667848619343]
  We introduce USTEP (Unified S-TEmporal Predictive learning), an innovative framework that reconciles the recurrent-based and recurrent-free methods by integrating both micro-temporal and macro-temporal scales.
  arXiv  Detail & Related papers  (2023-10-09T16:17:42Z)
• On Loop Formulas with Variables [2.1955512452222696]
  Recently Ferraris, Lee and Lifschitz proposed a new definition of stable models that does not refer to grounding. We show its relation to the idea of loop formulas with variables by Chen, Lin, Wang and Zhang. We extend the syntax of logic programs to allow explicit quantifiers, and define its semantics as a subclass of the new language of stable models.
  arXiv  Detail & Related papers  (2023-07-15T06:20:43Z)
• Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic [84.59255070520673]
  Large language models (LLMs) face a challenge when engaging in temporal reasoning. We propose TempLogic, a novel framework designed specifically for temporal question-answering tasks.
  arXiv  Detail & Related papers  (2023-05-24T10:57:53Z)
• Policy Analysis using Synthetic Controls in Continuous-Time [101.35070661471124]
  Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference. We propose a continuous-time alternative that models the latent counterfactual path explicitly using the formalism of controlled differential equations.
  arXiv  Detail & Related papers  (2021-02-02T16:07:39Z)
• From Static to Dynamic Node Embeddings [61.58641072424504]
  We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation. We find that the top-3 temporal models are always those that leverage the new $epsilon$-graph time-series representation.
  arXiv  Detail & Related papers  (2020-09-21T16:48:29Z)
• Temporal Answer Set Programming [3.263632801414296]
  We present an overview on Temporal Logic Programming under the perspective of its application for Knowledge Representation and declarative problem solving. We focus on recent results of the non-monotonic formalism called Temporal Equilibrium Logic (TEL) that is defined for the full syntax. In a second part, we focus on practical aspects, defining a syntactic fragment called temporal logic programs closer to ASP, and explain how this has been exploited in the construction of the solver TELINGO.
  arXiv  Detail & Related papers  (2020-09-14T16:13:36Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)
• Implementing Dynamic Answer Set Programming [0.0]
  We develop a translation of dynamic formulas into temporal logic programs. The reduction of dynamic formulas to temporal logic programs allows us to extend ASP with both approaches in a uniform way.
  arXiv  Detail & Related papers  (2020-02-17T12:34:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.