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Deal or No Deal!
Akash Sethiya
Training AI Bots To Negotiate
● Facebook Artificial Intelligence Research (FAIR)
● The main goal is to make the dialog agent with different goals to engage in
start-to-finish negotiations with other bots or people while arriving at common
decisions or outcomes.
● Capable of making novel sentences
● Hard Negotiation: It helds longer conversations with humans and don’t stop
until it achieves a successful outcome
● Researches uses a novel technique where an agent simulates a future
conversation by rolling out a dialog model to the end of the conversation.
Building a Dataset and Learning
● FAIR team crowdsourced a collection of negotiations between pairs of
peoples.
● Researchers trained a recurrent neural network to negotiate by teaching it
imitate people’s actions.
● Pre-training with supervised learning and then fine tuned the model against
the evaluation metric using reinforcement learning.
● At the end of every dialog, the agent is given a reward based in the deal it
agreed on. This reward was then back-propogated though every word that the
agent output using policy gradients to increase the probability of actions that
lead to high rewards.
Chatbot Conversation
Bob: I can can I I everything else
Alice: Balls have zero to me to me to me to me to me to me to
me to me to
Facebook shutdown the project after they find out that bots are
communicating with each other in a secretive shorthand english language.
New Video: https://www.youtube.com/watch?v=ONPqeHJShdQ
References
● https://code.facebook.com/posts/1686672014972296/deal-or-no-deal-
training-ai-bots-to-negotiate/
● https://www.youtube.com/watch?v=ONPqeHJShdQ
Questions?
Thank you

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Deal or no deal!

  • 1. Deal or No Deal! Akash Sethiya
  • 2. Training AI Bots To Negotiate ● Facebook Artificial Intelligence Research (FAIR) ● The main goal is to make the dialog agent with different goals to engage in start-to-finish negotiations with other bots or people while arriving at common decisions or outcomes. ● Capable of making novel sentences ● Hard Negotiation: It helds longer conversations with humans and don’t stop until it achieves a successful outcome ● Researches uses a novel technique where an agent simulates a future conversation by rolling out a dialog model to the end of the conversation.
  • 3.
  • 4. Building a Dataset and Learning ● FAIR team crowdsourced a collection of negotiations between pairs of peoples. ● Researchers trained a recurrent neural network to negotiate by teaching it imitate people’s actions. ● Pre-training with supervised learning and then fine tuned the model against the evaluation metric using reinforcement learning. ● At the end of every dialog, the agent is given a reward based in the deal it agreed on. This reward was then back-propogated though every word that the agent output using policy gradients to increase the probability of actions that lead to high rewards.
  • 5. Chatbot Conversation Bob: I can can I I everything else Alice: Balls have zero to me to me to me to me to me to me to me to me to Facebook shutdown the project after they find out that bots are communicating with each other in a secretive shorthand english language. New Video: https://www.youtube.com/watch?v=ONPqeHJShdQ