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              <p>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 whether to push an update to the AA and the query controller at the AA to pull updates by initiating queries at specific time instants. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating the updates' freshness, usefulness, and the timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of the 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 the 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 effectaware policies at both agents enhances effectiveness compared to periodic and/or probabilistic, effect-agnostic policies at either or both agents.</p>
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