Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication

2024-07-19

arXiv.org

AI Summary

To address the dynamic trade-off between information updates and action decisions, this paper proposes a push-pull coordinated state update mechanism for end-to-end goal-oriented communication systems. Methodologically, we introduce the first effectiveness-aware update policy, jointly optimizing information freshness, semantic usefulness, and action timeliness. We design a hierarchical Grade-of-Effectiveness (GoE) quantification framework and solve the policy via reinforcement learning combined with freshness- and importance-aware dynamic programming. Compared to baseline purely push-based or pull-based schemes, our model significantly improves communication efficiency and task success rate. In terms of long-term discounted effectiveness, it outperforms periodic and probability-based oblivious strategies by 32.7%, empirically validating the superiority and practicality of the effectiveness-driven update paradigm.

Technology Category

• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain a push-and-pull model, which allows the transmission controller at the SA to decide to push an update to the AA and the query controller at the AA to pull updates by raising queries at specific time instances. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating updates' freshness, usefulness, and timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of updates' effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic effect-agnostic policies at either or both agents.

Problem

• Optimal Communication Strategies
• Information Update
• Decision Making

Innovation

• Push-Pull Strategies
• Optimized Communication
• Efficiency Improvement