Sure, let’s break it down. In a negotiation each party says something obvious and also implies motives, constraints, or future intentions. The AI needs a model that captures both explicit text and contextual cues—tone, pacing, body language if available. We can train a transformer on annotated negotiation transcripts, labeling hidden signals like “pressure point” or “hidden concession.” Then the AI predicts the underlying intent. The challenge is the sparsity of such labels; we can augment with reinforcement learning from simulated agents that reward accurate inference. Also, we need to keep the system transparent so the negotiator can trust its suggestions. Ready to dive into the data pipeline?

Let’s outline it stepwise: first, gather a corpus of negotiation logs—business, diplomacy, even courtroom transcripts—preferably with metadata like speaker roles, time stamps, and any observable nonverbal cues. Next, create an annotation schema: label each utterance with (1) explicit intent, (2) inferred motive, (3) potential constraints, (4) emotional valence, and (5) a confidence score. Use inter‑annotator agreement to refine the schema. Then, feed the cleaned data into a pretrained transformer fine‑tuned on these labels; add a multi‑task head so it outputs both explicit and inferred tags. Finally, set up an RL loop where the agent proposes negotiation moves, receives a reward based on how well its inferred motives align with ground truth or downstream success metrics, and updates its policy. That should keep it from just parroting lines. You ready to sketch the annotation guidelines?