- In a new blog post, Facebook explains how existing chatbots can hold short conversations and perform simple tasks such as booking a restaurant – but building machines that can hold meaningful conversations with people is challenging because it requires a bot to combine its understanding of the conversation with its knowledge of the world, and then produce a new sentence that helps it achieve its goals.
- To help build their training set, the team created an interface with multi-issue bargaining scenarios and crowdsourced humans on Amazon Mechanical Turk to negotiate in natural language to divide a random set of objects.
- The models were trained end-to-end from the language and decisions that humans made, meaning that the approach can easily be adapted to other tasks.
- Reinforcement learning was then used to reward the model when it achieved a good outcome which prevents the AI bot from developing its own language.
- In their experiments, majority of the people didn’t know they were talking to a bot and FAIR’s best reinforcement learning negotiation agent matched the performance of human negotiators – achieving better deals about as often as worse deals.
Researchers at Facebook Artificial Intelligence Research (FAIR) published a paper introducing AI-based dialog agents that can negotiate and compromise.
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