In order to create interesting and engaging conversational interactions with users, open domain SocialBots need to interact using a range of dialogue acts (DAs). For example, a SocialBot should be able to ask factual and opinion questions, inform the user of facts and express opinions, agree and disagree with the user, provide appraisals and acknowledgements, make recommendations or suggestions, and confirm what the user said. For many applications it is also necessary to ground these DAs in knowledge of some kind, either structured or unstructured. In the past, such dialogue-act controlled response generation was typically trained from a large paired corpus that maps from a domain-specific meaning representation that specifies the desired DA and associated attributes, to one or more reference utterances. However recent advances in pretrained language models offer new possibilities for semantically controlled NLG. Here we show that we can achieve near perfect DA and semantic attribute control using Prompt-Based Transfer learning (PBL). We apply an overgenerate and rank method to compare eight few-shot prompt styles that include a novel method of generating from textual pseudo-references using a textual style transfer approach, a second novel approach that provides definitions of DAs in the prompts, inspired by previous work on schema-guided NLG, and a baseline of simply linearizing the MR. To our knowledge, this is the first work on NLG for dialogue that automatically evaluates and ranks outputs using DA accuracy. We then show that we can use PBL to successfully transfer these conversational DAs from WikiData triples in one domain, namely Video Games, to Wikidata triples in three other domains, namely Music, Movies and TV, providing a universal dialogue policy that can be used across all 4 domains in Athena, UCSC's Alexa Prize SocialBot.