Presentation on theme: "Emergence of Gricean Maxims from Multi-agent Decision Theory Adam Vogel Stanford NLP Group Joint work with Max Bodoia, Chris Potts, and Dan Jurafsky."— Presentation transcript:
Emergence of Gricean Maxims from Multi-agent Decision Theory Adam Vogel Stanford NLP Group Joint work with Max Bodoia, Chris Potts, and Dan Jurafsky
Decision-Theoretic Pragmatics Gricean cooperative principle: Make your contribution such as it is required, at the stage at which it occurs, by the accepted purpose or direction of the talk exchange in which you are engaged.
Decision-Theoretic Pragmatics Gricean Maxims: Be truthful: speak with evidence Be relevant: speak in accordance with goals Be clear: be brief and avoid ambiguity Be informative: say exactly as much as needed
Emergence of Gricean Maxims Co-operative principle Be truthful Be relevant Be clear Be informative ??? Approach: Operationalize the co-operative principle Tool: Multi-agent decision theory Goal: Maxims emerge from rational behavior Joint utility Rationality
Related Work One-shot reference tasks – Generating spatial referring expressions [Golland et al. 2010] – Predicting pragmatic reasoning in language games [Stiller et al. 2011] Interpreting natural language instructions – Learning to read help guides [Branavan et al. 2009] – Learning to following navigational directions [Vogel and Jurafsky 2010] [Artzi and Zettlemoyer 2013] [Chen and Mooney 2011] [Tellex et al. 2011]
Return to Grice Be truthful: DialogBot speaks with evidence Be relevant: DialogBot gives advice to help win the game Be clear Be informative
Experimental Results Evaluate pairs of agents from 197 random initial states Agents have 50 high-level moves to find the card Bots% SuccessAverage High Level Actions ListenerBot & ListenerBot 84.4%19.8 ListenerBot & DialogBot 87.2%17.5 DialogBot & DialogBot 90.6%16.6
Emergent Gricean Behavior Be truthful: DialogBot speaks with evidence Be relevant: DialogBot gives advice to help win Be clear: need variable costs on messages Be informative: requires levels of specificity ACL 2013: Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs From joint reward, not hard coded Future Work: intentions, joint plans, deeper belief nesting Thanks!