Towards Preserving Semantic Structure in Argumentative Multi-Agent via Abstract Interpretation

• URL: http://arxiv.org/abs/2211.15782v1
• Date: Mon, 28 Nov 2022 21:32:52 GMT
• Title: Towards Preserving Semantic Structure in Argumentative Multi-Agent via Abstract Interpretation
• Authors: Minal Suresh Patil
• Abstract summary: We investigate the notion of abstraction from the model-checking perspective. Several arguments are trying to defend the same position from various points of view, thereby reducing the size of the argumentation framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the recent twenty years, argumentation has received considerable attention in the fields of knowledge representation, reasoning, and multi-agent systems. However, argumentation in dynamic multi-agent systems encounters the problem of significant arguments generated by agents, which comes at the expense of representational complexity and computational cost. In this work, we aim to investigate the notion of abstraction from the model-checking perspective, where several arguments are trying to defend the same position from various points of view, thereby reducing the size of the argumentation framework whilst preserving the semantic flow structure in the system.

Related papers

• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• A Methodology for Gradual Semantics for Structured Argumentation under Incomplete Information [15.717458041314194]
  We provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks. Our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology.
  arXiv  Detail & Related papers  (2024-10-29T16:38:35Z)
• Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation [19.799266797193344]
  Argumentation-based systems often lack explainability while supporting decision-making processes. Counterfactual and semifactual explanations are interpretability techniques. We show that counterfactual and semifactual queries can be encoded in weak-constrained Argumentation Framework.
  arXiv  Detail & Related papers  (2024-05-07T07:27:27Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• Slot Abstractors: Toward Scalable Abstract Visual Reasoning [5.262577780347204]
  We propose Slot Abstractors, an approach to abstract visual reasoning that can be scaled to problems involving a large number of objects and multiple relations among them. The approach displays state-of-the-art performance across four abstract visual reasoning tasks, as well as an abstract reasoning task involving real-world images.
  arXiv  Detail & Related papers  (2024-03-06T04:49:02Z)
• On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts [51.3324922038486]
  This paper reports the empirical results of the interplay of prompts and discussion mechanisms. It also proposes a scalable discussion mechanism based on conquer and merge.
  arXiv  Detail & Related papers  (2023-11-13T04:56:48Z)
• A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
  We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
  arXiv  Detail & Related papers  (2023-10-18T20:18:05Z)
• Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings [61.04460792203266]
  We introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks.
  arXiv  Detail & Related papers  (2023-05-03T17:58:29Z)
• A Formalisation of Abstract Argumentation in Higher-Order Logic [77.34726150561087]
  We present an approach for representing abstract argumentation frameworks based on an encoding into classical higher-order logic. This provides a uniform framework for computer-assisted assessment of abstract argumentation frameworks using interactive and automated reasoning tools.
  arXiv  Detail & Related papers  (2021-10-18T10:45:59Z)

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.