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This paper describes a model of conversation strategies implemented in virtual humans designed to help people learn negotiation skills. We motivate and discuss these strategies and their use to allow a virtual human to engage in complex adversarial negotiation with a human trainee. Choice of strategy depends on both the personality of the agent and assessment of the likelihood that the negotiation can be beneficial. Execution of strategies can be performed by choosing specific dialogue behaviors such as whether and how to respond to a proposal. Current assessment of the value of the topic, the utility of the strategy, and affiliation toward the other conversants can be used to dynamically change strategies throughout the course of a conversation. Examples will be given from the SASO-ST project, in which a trainee learns to negotiate by interacting with virtual humans who employ these strategies.