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

• Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models [76.6028674686018]
  We introduce thought-tracing, an inference-time reasoning algorithm to trace the mental states of agents. Our algorithm is modeled after the Bayesian theory-of-mind framework. We evaluate thought-tracing on diverse theory-of-mind benchmarks, demonstrating significant performance improvements.
  arXiv  Detail & Related papers  (2025-02-17T15:08:50Z)
• (Neural-Symbolic) Machine Learning for Inconsistency Measurement [0.0]
  We present machine-learning-based approaches for determining the emphdegree of inconsistency -- which is a numerical value -- for propositional logic knowledge bases. Specifically, we present regression- and neural-based models that learn to predict the values that the inconsistency measures $incmi$ and $incat$ would assign to propositional logic knowledge bases.
  arXiv  Detail & Related papers  (2025-02-05T08:00:30Z)
• Instantiation-based Formalization of Logical Reasoning Tasks using Language Models and Logical Solvers [4.897782942277061]
  We introduce Semantic Self-Verification (SSV), a novel approach to accurately formulate the reasoning problem from natural language to the formal language of the solver. SSV uses a consistency-based approach to produce strong abstract formalizations of problems using concrete instantiations that are generated by the model and verified by the solver. We propose such *near-certain reasoning* as a new approach to reduce the need for manual verification in many cases, taking us closer to more dependable and autonomous AI reasoning systems.
  arXiv  Detail & Related papers  (2025-01-28T14:04:49Z)
• 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)
• 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)
• 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)

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.