Logical inference and polyhedral projection

2005 ◽  
pp. 184-200 ◽  
Author(s):  
J. N. Hooker
Keyword(s):  
Logical Inference ◽  
Polyhedral Projection

scholarly journals Reasoning Through the Disjunctive Syllogism in Monkeys

2021 ◽  
Vol 32 (2) ◽  
pp. 292-300
Author(s):  
Stephen Ferrigno ◽  
Yiyun Huang ◽  
Jessica F. Cantlon
Keyword(s):  
Decision Making ◽  
Food Choice ◽  
Human Learning ◽  
Choice Task ◽  
Logical Inference ◽  
Critical Aspect ◽  
Disjunctive Syllogism ◽  
Symbolic Thought ◽  
Dependent Relation ◽  
Nonhuman Animals

The capacity for logical inference is a critical aspect of human learning, reasoning, and decision-making. One important logical inference is the disjunctive syllogism: given A or B, if not A, then B. Although the explicit formation of this logic requires symbolic thought, previous work has shown that nonhuman animals are capable of reasoning by exclusion, one aspect of the disjunctive syllogism (e.g., not A = avoid empty). However, it is unknown whether nonhuman animals are capable of the deductive aspects of a disjunctive syllogism (the dependent relation between A and B and the inference that “if not A, then B” must be true). Here, we used a food-choice task to test whether monkeys can reason through an entire disjunctive syllogism. Our results show that monkeys do have this capacity. Therefore, the capacity is not unique to humans and does not require language.

Colligation or the logical inference of interconnection

2000 ◽  
Vol 52 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Ole Immanuel Franksen ◽  
Peter Falster
Keyword(s):  
Logical Inference

scholarly journals A Semantic Web-based Representation of Human-logical Inference for Solving Bongard Problems

2020 ◽  
Vol 26 (10) ◽  
pp. 1343-1363
Author(s):  
Jisha Maniamma ◽  
Hiroaki Wagatsuma
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Description Logic ◽  
Logical Reasoning ◽  
Hierarchical Level ◽  
Logical Inference ◽  
Learning Abilities ◽  
Web Based ◽  
Wide Range ◽  
Target Frame

Bongard Problems (BPs) are a set of 100 visual puzzles introduced by M. M. Bongard in the mid-1960s. BPs have been established as benchmark puzzles for understanding the human context-based learning abilities to solve ill- posed problems. The puzzle requires the logical explanation as the answer to distinct two classes of figures from redundant options, which can be obtained by a thinking process to alternatively change the target frame (hierarchical level of analogy) of thinking from a wide range concept networks as D. R. Hofstadter suggested. Some minor research results to solve a limited set of BPs have reported based a single architecture accompanied with probabilistic approaches; however the central problem on BP's difficulties is the requirement of flexible changes of the target frame, therefore non-hierarchical cluster analyses does not provide the essential solution and hierarchical probabilistic models needs to include unnecessary levels for learning from the beginning to prevent a prompt decision making. We hypothesized that logical reasoning process with limited numbers of meta-data descriptions realizes the sophisticated and prompt decision-making and the performance is validated by using BPs. In this study, a semantic web-based hierarchical model to solve BPs was proposed as the minimum and transparent system to mimic human-logical inference process in solving of BPs by using the Description Logic (DL) with assertions on concepts (TBox) and individuals (ABox). Our results demonstrated that the proposed model not only provided individual solutions as a BP solver, but also proved the correctness of Hofstadter's idea as the flexible frame with concept networks for BPs in our actual implementation, which no one has ever achieved. This fact will open the new horizon for theories for designing of logical reasoning systems especially for critical judgments and serious decision-making as expert humans do in a transparent and descriptive way of why they judged in that manner.

scholarly journals A Talk on the Essence of Circular Cities

2020 ◽  
Vol 3 (4) ◽  
Author(s):  
An-Pi Chang
Keyword(s):  
Policy Implementation ◽  
Circular Economy ◽  
Research Method ◽  
City Planning ◽  
Logical Inference ◽  
The Sustainable Development ◽  
Expert Interview ◽  
Starting Point ◽  
Fishbone Diagram ◽  
Resource Conditions

Research on the essence of policy implementation is the basis for finding solutions. A circular city is founded on the concept of a circular economy, extending from the recycling of single substances to regional resource recycling development. Given limited energy and resource conditions, the emphasis lies in considering right from that source that at the end of a product’s service life substances can continue to enter their cycle of re-use and re-utilization. Meanwhile, residual substances can return to the industry and organisms as basic nutrients. The development of circular cities has to be multi-faceted synergetic promotion. Otherwise, it will be deviating from the meaning of the circular essence. In this study, the sustainable development of environment, economy, society and governance aspects were adopted as the starting point for exploring the connotation of the promotion of circular cites. The semi-structured expert interview was adopted as the research method. The pyramid principle was employed to carry out logical inference. The Fishbone Diagram was used to carry out time series analysis in order to ensure relevant requirements do not deviate from the mindset of circular essence during circular city planning. Finally, the 13 circular city planning solutions proposed in the research results and contribution can be specifically provided to agencies engaged in circular city planning and governance. They shall also serve as a reference for circular city solutions.

scholarly journals 24 Challenges in Deductive Software Verification

10.29007/j2cm ◽  
2018 ◽  
Author(s):  
Reiner Hähnle ◽  
Marieke Huisman
Keyword(s):  
Software Verification ◽  
Logical Inference ◽  
Verification Process ◽  
Further Development

Deductive software verification aims at formally verifying that all possible behaviors of a given program satisfy formally defined, complex properties, where the verification process is based on logical inference. We list the most important challenges for the further development of the field.

scholarly journals Multimodal Logical Inference System for Visual-Textual Entailment

2019 ◽  
Author(s):  
Riko Suzuki ◽  
Hitomi Yanaka ◽  
Masashi Yoshikawa ◽  
Koji Mineshima ◽  
Daisuke Bekki
Keyword(s):  
Logical Inference ◽  
Inference System ◽  
Textual Entailment

scholarly journals An Investigation about Entailment and Narrative by AI Techniques (Generative Models)

2020 ◽  
Vol 3 (4) ◽  
pp. p61
Author(s):  
Paolo Marocco ◽  
Roberto Gigliucci
Keyword(s):  
Generative Models ◽  
Language Models ◽  
Original Text ◽  
Logical Inference ◽  
Narrative Story ◽  
Task Requirements ◽  
Lyric Tradition

Many storytelling generation problems concern the difficulty to model the sequence of sentences. Language models are generally able to assign high scores to well-formed text, especially in the cases of short texts, failing when they try to simulate human textual inference. Although in some cases output text automatically generated sounds as bland, incoherent, repetitive and unrelated to the context, in other cases the process reveals capability to surprise the reader, avoiding to be boring/predictable, even if the generated text satisfies entailment task requirements. The lyric tradition often does not proceed towards a real logical inference, but takes into account alternatives like the unexpectedness, useful for predicting when a narrative story will be perceived as interesting. To achieve a best comprehension of narrative variety, we propose a novel measure based on two components: inference and unexpectedness, whose different weights can modify the opportunity for readers to have different experiences about the functionality of a generated story. We propose a supervised validation treatment, in order to compare the authorial original text, learned by the model, with the generated one.

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