Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

• URL: http://arxiv.org/abs/2106.14566v1
• Date: Mon, 28 Jun 2021 10:43:25 GMT
• Title: Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming
• Authors: Joaqu\'in Arias and Manuel Carro and Zhuo Chen and Gopal Gupta
• Abstract summary: Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks.
• Score: 8.677108656718824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

Related papers

• Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE [68.6018458996143]
  We propose a more general dynamic network that can combine both quantization and early exit dynamic network: QuEE. Our algorithm can be seen as a form of soft early exiting or input-dependent compression. The crucial factor of our approach is accurate prediction of the potential accuracy improvement achievable through further computation.
  arXiv  Detail & Related papers  (2024-06-20T15:25:13Z)
• Algorithmic syntactic causal identification [0.8901073744693314]
  Causal identification in causal Bayes nets (CBNs) is an important tool in causal inference. Most existing formulations of causal identification using techniques such as d-separation and do-calculus are expressed within the mathematical language of classical probability theory. We show that this restriction can be lifted by replacing the use of classical probability theory with the alternative axiomatic foundation of symmetric monoidal categories.
  arXiv  Detail & Related papers  (2024-03-14T17:14:53Z)
• Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement [92.61557711360652]
  Language models (LMs) often fall short on inductive reasoning, despite achieving impressive success on research benchmarks. We conduct a systematic study of the inductive reasoning capabilities of LMs through iterative hypothesis refinement. We reveal several discrepancies between the inductive reasoning processes of LMs and humans, shedding light on both the potentials and limitations of using LMs in inductive reasoning tasks.
  arXiv  Detail & Related papers  (2023-10-12T17:51:10Z)
• Large Language Models as Analogical Reasoners [155.9617224350088]
  Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks. We introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models.
  arXiv  Detail & Related papers  (2023-10-03T00:57:26Z)
• Chaining Simultaneous Thoughts for Numerical Reasoning [92.2007997126144]
  numerical reasoning over text should be an essential skill of AI systems. Previous work focused on modeling the structures of equations, and has proposed various structured decoders. We propose CANTOR, a numerical reasoner that models reasoning steps using a directed acyclic graph.
  arXiv  Detail & Related papers  (2022-11-29T18:52:06Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Efficient Knowledge Compilation Beyond Weighted Model Counting [7.828647825246474]
  We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems. First level techniques based on Knowledge Compilation (KC) have been adapted for specific 2AMC instances by imposing variable order constraints. We show that we can exploit the logical structure of a 2AMC problem to omit parts of these constraints, thus limiting the negative effect.
  arXiv  Detail & Related papers  (2022-05-16T08:10:40Z)
• Conditional independence by typing [30.194205448457385]
  A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. Conditional independence (CI) relationships among parameters are a crucial aspect of probabilistic models. We show that for a well-typed program in our system, the distribution it implements is guaranteed to have certain CI-relationships.
  arXiv  Detail & Related papers  (2020-10-22T17:27:22Z)
• Plausible Reasoning about EL-Ontologies using Concept Interpolation [27.314325986689752]
  We propose an inductive mechanism which is based on a clear model-theoretic semantics, and can thus be tightly integrated with standard deductive reasoning. We focus on inference, a powerful commonsense reasoning mechanism which is closely related to cognitive models of category-based induction.
  arXiv  Detail & Related papers  (2020-06-25T14:19:41Z)
• From Checking to Inference: Actual Causality Computations as Optimization Problems [79.87179017975235]
  We present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems. We show that both notions are efficiently automated. Using models with more than $8000$ variables, checking is computed in a matter of seconds, with MaxSAT outperforming ILP in many cases.
  arXiv  Detail & Related papers  (2020-06-05T10:56:52Z)
• On Quantified Modal Theorem Proving for Modeling Ethics [0.0]
  A quantified modal logic has been used to model various versions of the doctrine of double effect, akrasia, and virtue ethics. We outline these distinct characteristics and present a sketches of an algorithm that can help with some aspects proof automation for DCEC.
  arXiv  Detail & Related papers  (2019-12-30T15:14:21Z)

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.