Reasoning with the Theory of Mind for Pragmatic Semantic Communication
- URL: http://arxiv.org/abs/2311.18224v1
- Date: Thu, 30 Nov 2023 03:36:19 GMT
- Title: Reasoning with the Theory of Mind for Pragmatic Semantic Communication
- Authors: Christo Kurisummoottil Thomas and Emilio Calvanese Strinati and Walid
Saad
- Abstract summary: A pragmatic semantic communication framework is proposed in this paper.
It enables effective goal-oriented information sharing between two-intelligent agents.
Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits.
- Score: 62.87895431431273
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, a pragmatic semantic communication framework that enables
effective goal-oriented information sharing between two-intelligent agents is
proposed. In particular, semantics is defined as the causal state that
encapsulates the fundamental causal relationships and dependencies among
different features extracted from data. The proposed framework leverages the
emerging concept in machine learning (ML) called theory of mind (ToM). It
employs a dynamic two-level (wireless and semantic) feedback mechanism to
continuously fine-tune neural network components at the transmitter. Thanks to
the ToM, the transmitter mimics the actual mental state of the receiver's
reasoning neural network operating semantic interpretation. Then, the estimated
mental state at the receiver is dynamically updated thanks to the proposed
dynamic two-level feedback mechanism. At the lower level, conventional channel
quality metrics are used to optimize the channel encoding process based on the
wireless communication channel's quality, ensuring an efficient mapping of
semantic representations to a finite constellation. Additionally, a semantic
feedback level is introduced, providing information on the receiver's perceived
semantic effectiveness with minimal overhead. Numerical evaluations demonstrate
the framework's ability to achieve efficient communication with a reduced
amount of bits while maintaining the same semantics, outperforming conventional
systems that do not exploit the ToM-based reasoning.
Related papers
- Variational Source-Channel Coding for Semantic Communication [6.55201432222942]
The current semantic communication systems are generally modeled as an Auto-Encoder (AE)
AE lacks a deep integration of AI principles with communication strategies due to its inability to effectively capture channel dynamics.
This paper explores the inclusion of data distortion distinguishes semantic communication from classical communication.
A Variational Source-Channel Coding (VSCC) method is proposed for constructing semantic communication systems.
arXiv Detail & Related papers (2024-09-26T03:42:05Z) - Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware
Communication Framework [124.6509194665514]
A novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users.
A projection-based semantic encoder is proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission.
A generative adversarial imitation learning-based solution, called G-RML, is proposed to enable the destination user to learn and imitate the implicit semantic reasoning process of source user.
arXiv Detail & Related papers (2023-06-20T01:32:27Z) - Cognitive Semantic Communication Systems Driven by Knowledge Graph:
Principle, Implementation, and Performance Evaluation [74.38561925376996]
Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios.
An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph.
For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
arXiv Detail & Related papers (2023-03-15T12:01:43Z) - Imitation Learning-based Implicit Semantic-aware Communication Networks:
Multi-layer Representation and Collaborative Reasoning [68.63380306259742]
Despite its promising potential, semantic communications and semantic-aware networking are still at their infancy.
We propose a novel reasoning-based implicit semantic-aware communication network architecture that allows multiple tiers of CDC and edge servers to collaborate.
We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users.
arXiv Detail & Related papers (2022-10-28T13:26:08Z) - Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent
Semantic Communications [71.63189900803623]
A novel emergent SC system framework is proposed and is composed of a signaling game for emergent language design and a neuro-symbolic (NeSy) artificial intelligence (AI) approach for causal reasoning.
The ESC system is designed to enhance the novel metrics of semantic information, reliability, distortion and similarity.
arXiv Detail & Related papers (2022-10-21T15:33:37Z) - One-to-Many Semantic Communication Systems: Design, Implementation,
Performance Evaluation [35.21413988605204]
We propose a one-to-many semantic communication system called MR_DeepSC.
By leveraging semantic features for different users, a semantic recognizer is built to distinguish different users.
The proposed MR_DeepSC can achieve the best performance in terms of BLEU score.
arXiv Detail & Related papers (2022-09-20T02:48:34Z) - Communication Beyond Transmitting Bits: Semantics-Guided Source and
Channel Coding [7.080957878208516]
"Semantic communications" offers promising research direction.
Injecting semantic guidance into the coded transmission design to achieve semantics-aware communications shows great potential for breakthrough in effectiveness and reliability.
This article sheds light on semantics-guided source and channel coding as a transmission paradigm of semantic communications.
arXiv Detail & Related papers (2022-08-04T06:12:55Z) - Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic
Communication [85.06664206117088]
6G networks must consider semantics and effectiveness (at end-user) of the data transmission.
NeSy AI is proposed as a pillar for learning causal structure behind the observed data.
GFlowNet is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data.
arXiv Detail & Related papers (2022-05-22T07:11:57Z) - Semantics-Native Communication with Contextual Reasoning [46.2484183677342]
We propose a novel model of System 1 semantics-native communication (SNC) for generic tasks.
We infuse contextual reasoning into SNC such that the speaker locally and iteratively self-communicates with a virtual agent built on the listener's unique way of its semantics.
It is also shown that System 2 SNC significantly reduces the SR length without compromising communication reliability.
arXiv Detail & Related papers (2021-08-12T12:04:27Z)
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.