AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The introduction of Multi-Agent Reward Prediction (MARP) demonstrates that emergent multi-agent behavior can be treated not only as a phenomenon to study, but as a target of principled, data-driven regulation.
Experimental evaluations demonstrate that the PTO framework enhances dialogue agents' performance in goal-oriented conversations within the domain of Motivational Interviewing, and incorporating look-ahead simulations led to improved long-term planning and more effective conversational strategies.
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