PaperlayerView exact revision on arXiv

Source-linked AI summary

Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication

Christo Kurisummoottil Thomas, Walid Saad

arXiv:2205.10768v1cs.LGcs.AIcs.ITmath.IT

TL;DR

Intent-based communication seeks to transmit useful meaning with fewer bits, while prior approaches often lack reasoning about how data are generated. The paper proposes NeSy AI with GFlowNets to learn causal structure and optimize encoding and decoding; simulations report fewer bits for the same semantics than classical communication.

  • Problem

    Conventional wireless systems focus on reliably sending physical bits without emphasizing data semantics or effectiveness, while prior semantic-communication work often omits reasoning about data generation.

    Source: §I · p. 1

  • Method

    The paper combines symbolic knowledge bases with neural learning, using GFlowNets to learn causal structure and train encoder/decoder functions for intent-based semantic communication.

    Source: §I · pp. 1–2, A. Semantic Language Construction · p. 3

  • Results

    The semantic system transmits 100 times fewer bits than the classical system for the same amount of semantic information, and shows significant improvement over ProbLog-based communication in decoding error probability versus p for the BSC.

    Source: §IV · p. 6

  • Takeaways & Limitations

    Simulations show that NeSy-based semantic communication can convey the same semantics with fewer bits than conventional communication while retaining meaning or effectiveness at the listener.

    Source: §I · p. 2, §IV · p. 6

Abstract

from arXiv · show

Intent-based networks that integrate sophisticated machine reasoning technologies will be a cornerstone of future wireless 6G systems. Intent-based communication requires the network to consider the semantics (meanings) and effectiveness (at end-user) of the data transmission. This is essential if 6G systems are to communicate reliably with fewer bits while simultaneously providing connectivity to heterogeneous users. In this paper, contrary to state of the art, which lacks explainability of data, the framework of neuro-symbolic artificial intelligence (NeSy AI) is proposed as a pillar for learning causal structure behind the observed data. In particular, the emerging concept of generative flow networks (GFlowNet) is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data. Further, a novel optimization problem for learning the optimal encoding and decoding functions is rigorously formulated with the intent of achieving higher semantic reliability. Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability. These semantic measure functions rely on the proposed definition of semantic content of the knowledge base and this information measure is reflective of the nodes' reasoning capabilities. Simulation results validate the ability to communicate efficiently (with less bits but same semantics) and significantly better compared to a conventional system which does not exploit the reasoning capabilities.

I. INTRODUCTION

Intent-based semantic communication aims to transmit useful meaning rather than all observed data, but prior approaches often omit the reasoning behind data generation. The paper proposes a NeSy AI framework that learns causal structure and optimizes messages for semantic reliability, reporting fewer transmitted bits than conventional communication.

I. INTRODUCTION

  • Prior semantic-communication approaches often omit reasoning about data generation and may not generalize to situations unseen during training [4]–[7].The paper also notes that one ProbLog approach adds a semantic layer over technical communication [5].Source: §I · p. 1
  • NeSy AI combines symbolic semantic representations with neural learning and deduction to extract receiver-meaningful semantics and optimize transmitted messages.The knowledge base represents the symbolic component, while GFlowNet learns reasoning and encoder/decoder functions.Source: §I · p. 1
  • The proposed semantic information measure uses the confidence of logical formulas represented by a knowledge base at an edge node.Source: §I · p. 2
  • The optimization objective accounts for channel effects, listener knowledge-base imperfections, and learning the Bayesian network structure that generates observed data.Source: §I · p. 2
  • GFlowNet training solves for encoder/decoder functions, and simulations report fewer bits than conventional communication while retaining meaning and effectiveness at the listener.The paper presents this as its first use of NeSy AI for an intent-based semantic-communication model.Source: §I · p. 2

II. PROPOSED SC SYSTEM MODEL

The system models communication as a speaker describing sequentially observed events to a listener, who acts on the received information to complete a task. It focuses on reasoning over event data and selecting meaningful messages rather than transmitting all acquired information.

II. PROPOSED SC SYSTEM MODEL

  • A task comprises sequential events observed by a speaker, who describes them to a listener that must act to advance the task.Source: §II · p. 2
  • The flying-robot example motivates transmitting meaningful data rather than all observations, which may include unnecessary background information and create communication bottlenecks.Source: §II · p. 2
  • The system equips end nodes with a knowledge base and reasoning to deduce logical conclusions, focusing on causal structure learning and optimal message choice.Source: §II · p. 2

A. Semantic Language Construction

The semantic language represents events through entities, attributes, and relations so that speaker and listener can communicate concise descriptions. The section then uses causal structure learning to model state dependencies and explain observed events.

A. Semantic Language Construction

  • A semantic language lets the speaker describe an event with a concise state description instead of transmitting all acquired data.The language rules are agreed upon between speaker and listener and represent relevant entities, attributes, and relations.Source: A. Semantic Language Construction · p. 2
  • A knowledge base is K = {E, P, R, F}, with facts represented as entity-relation-entity triplets; state descriptions conjoin atomic sentences, while relations among entities add semantic information.Source: Figure 1 · p. 3
  • Causal structure learning estimates posterior dependencies among event attributes, and GFlowNets are described as generative models able to generalize to states unseen during training.Source: A. Semantic Language Construction · p. 3
  • The state sequence g represents an event, while a binary adjacency matrix W records directed edges among the selected entities and attributes; GFlowNet parameters are denoted by θ.The conditional distribution is p(si | pa(si)), where pa(si) denotes the parent nodes of si.Source: A. Semantic Language Construction · p. 3
  • The GFlowNet uses separate output heads to propose valid DAG edge additions and to terminate a trajectory, forming a probabilistic graphical model for the causal structure.Source: A. Semantic Language Construction · p. 3

C. Grounding of Symbolic Elements

Symbol grounding maps learned causal structure to a grounded state description and makes logical symbols physically interpretable for communication. The paper represents grounded symbols as tensors and uses fuzzy semantics to express degrees of truth or relation confidence.

C. Grounding of Symbolic Elements

D. Semantic Information Content of the KB

The paper defines a theory’s semantic content by aggregating the groundings of its logical formulas. The measure is built on a real-logic theory comprising a knowledge base, a grounding function, and learned grounding parameters.

D. Semantic Information Content of the KB

  • Real logic represents a theory as T = (K, Gθ(·), Θ), with a knowledge base, a grounding function, and parameters learned through a DNN.The knowledge base contains logical symbols; the parameters θs belong to Θs ⊂ Θ.Source: D. Semantic Information Content of the… · p. 4
  • A theory’s semantic content is the aggregation of grounding values across the logical formulas in its knowledge base.The aggregation operator may use different implementations, such as the mean, minimum, or harmonic mean.Source: D. Semantic Information Content of the… · p. 4
  • The measure extends Shannon’s transmission-focused view by treating the transmitter and receiver as intelligent agents with reasoning capabilities.The authors motivate the measure using information measures defined in.Source: D. Semantic Information Content of the… · p. 4

III. ENCODER/DECODER PROBLEM FORMULATION: OPTIMIZING TRANSMIT MESSAGE

The system encodes a grounded state description into a transmit message and decodes the received signal into a reconstructed message. Its formulation accounts for channel effects and differences between the speaker’s and listener’s evolving knowledge bases.

III. ENCODER/DECODER PROBLEM FORMULATION: OPTIMIZING TRANSMIT MESSAGE

  • The transmitter maps grounded state description z to message m, while the receiver maps signal x to a reconstructed message; encoder weights remain fixed during a task.The state description is assumed given for the observed event, with causal structure learned using GFlowNet.Source: §III · p. 4
  • Channel errors and an inaccurate listener knowledge base can make reconstruction of the state description imperfect, despite error-free decoding under an error-free channel.The listener acts using its evolving knowledge base and the received semantic message.Source: §III · p. 5
  • Semantic information is tracked in the speaker’s and listener’s knowledge bases as their contents change with each updated state.The update expression associates changes in semantic content with grounding contributions from the changed knowledge base.Source: §III · p. 5

A. Semantic Distortion and Semantic Similarity

The paper measures semantic distortion and similarity separately from bit error rate, then uses these measures to characterize semantic reliability. Its training procedure updates the GFlowNet and encoder-decoder parameters for transmission.

A. Semantic Distortion and Semantic Similarity

  • Semantic distortion includes squared error between transmitted and learned messages and squared difference between the semantic information in the speaker’s and listener’s knowledge bases.The information difference quantifies how much semantic information the listener extracts from an inaccurate received message.Source: A. Semantic Distortion and Semantic… · p. 5
  • Semantic similarity is not bit error rate: differently worded messages can represent the same state and preserve the intended meaning despite syntactic errors.The paper illustrates this with a received sentence containing typos that remains semantically recoverable.Source: A. Semantic Distortion and Semantic… · p. 5
  • The evolving-KB training algorithm samples minibatches of events, updates GFlowNet and encoder-decoder parameters, then uses the learned weights for transmission.The algorithm initializes the threshold µ to 0.4 and stops event training when P(sf | G(t−1)) exceeds µ.Source: A. Semantic Distortion and Semantic… · p. 5
  • Semantic distortion need not be arbitrarily small for reliability to be very close to 1, unlike the classical BER measure.The paper also defines causal influence on semantic communication to capture message impact through the channel and discrepancies between the speaker’s and listener’s knowledge bases.Source: A. Semantic Distortion and Semantic… · p. 5

B. SC Message Transmission Problem Formulation

The message-transmission objective selects a message to represent transmitter-side semantics while meeting a semantic-reliability target. It also optimizes GFlowNet parameters for grounding, with a reward tied to the average number of bits used.

B. SC Message Transmission Problem Formulation

  • Semantic reliability is the probability that semantic distortion falls below δ, enabling reconstruction of the logical formulas in the decoded state description.The formulation allows meaning to be recovered even with larger BER when semantic distortion remains within the limit.Source: B. SC Message Transmission Problem… · p. 5
  • The formulation permits δ and ǫ to be arbitrarily small values greater than zero.Source: B. SC Message Transmission Problem… · p. 5
  • The GFlowNet loss optimizes parameters for optimal grounding, and its reward can be chosen inversely proportional to the average bits needed to represent a grounding.The flow is parameterized by a GFlowNet neural network and computed during training.Source: B. SC Message Transmission Problem… · p. 5

C. Proposed NeSy AI Algorithm for Transmit Message Design

The objective function is designed to capture end nodes’ semantic reasoning capabilities and achieve high semantic reliability. The method fine-tunes the GFlowNet and encoder/decoder weights, with the paper asserting convexity and convergence to a global minimum.

C. Proposed NeSy AI Algorithm for Transmit Message Design

IV. SIMULATION RESULTS

The simulations report fewer transmitted bits for NeSy AI semantic communication than for classical communication, while preserving meaning, and improved performance over ProbLog-based semantic communication. The experiments use generated causal data, a GFlowNet, and a binary symmetric channel.

IV. SIMULATION RESULTS

  • The experiments train on 25,000 generated observations and send compressed messages over a binary symmetric channel.The generated data follows a random-graph model, and the GFlowNet learns its causal structure.Source: §IV · p. 6
  • The NeSy AI semantic system transmits 100 times fewer bits than classical communication to convey the same semantic information.The comparison considers the bits needed to reach a particular error probability over 10,000 events.Source: §IV · p. 6
  • NeSy AI-based semantic communication significantly improves over ProbLog-based semantic communication in decoding error probability versus BSC crossover probability.Source: §IV · p. 6

V. CONCLUSION

The conclusion presents NeSy AI as a semantic-communication model that brings intelligence to end nodes. It combines a knowledge base and GFlowNet reasoning with causal-structure learning and optimized encoder/decoder functions, and reports gains in bits for conveying the same meaning reliably.

V. CONCLUSION

  • The conclusion describes NeSy AI as a semantic-communication model intended to bring intelligence to end nodes.Source: §V · p. 6
  • Figure 2 frames the comparison as NeSy AI-based semantics versus classical communication.Source: Figure 2 · p. 6
  • Simulations show significant gains in physical bits transmitted to convey the same meaning reliably compared with a classical network.Source: Figure 3 · p. 6
  • The system uses a knowledge base for its symbolic component and a GFlowNet to enable reasoning.Source: Figure 3 · p. 6
  • The paper formulates an optimization problem for learning causal structure from data and optimal encoder or decoder functions.Source: Figure 3 · p. 6
Loading 2205.10768v1…