I am worried when computers start getting better than people at these kinds of things. They already mastered heads-up poker.
Almost all of our systems rely on an inefficiency of an attacker - so they are vulnerable.
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I am worried when computers start getting better than people at these kinds of things. They already mastered heads-up poker.
Almost all of our systems rely on an inefficiency of an attacker - so they are vulnerable.
Interesting that the chatbots learned to show "fake" interest in an item, just to conceide it later in the negotiation process. But I think what is missing, is the time component when negotiating with humans. A negotiation process is usually better for humans if the negotiation is quick and not dragging on too long. And more importantly the chatbots never seemed to "walk away" from a deal. But in real life, you somet…
I bet you could make a walking away function with a threshold and that would trigger a iterated prisoner dilemma subroutine.
Imagine a chatbot who can chat up a girl online better than any human. Whose jokes make any human seel dull by comparison. And whose wit is quick to about 1 trillion jokes a second :-P
It'd be interesting if the agents developed their own language during the reinforcement learning stage that is unintelligible to humans but allows them to quickly navigate the negotiation. They use the model trained in a supervised way during the reinforcement learning stage to avoid this, but I'm curious to see what the agent learns when paired against another reinforcement learning agent. Edit: Indeed, the paper sa…
It'd be interesting if the agents developed their own language during the reinforcement learning stage that is unintelligible to humans but allows them to quickly navigate the negotiation. They use the model trained in a supervised way during the reinforcement learning stage to avoid this, but I'm curious to see what the agent learns when paired against another reinforcement learning agent. Edit: Indeed, the paper sa…
Interesting that the chatbots learned to show "fake" interest in an item, just to conceide it later in the negotiation process. But I think what is missing, is the time component when negotiating with humans. A negotiation process is usually better for humans if the negotiation is quick and not dragging on too long. And more importantly the chatbots never seemed to "walk away" from a deal. But in real life, you somet…
> But in real life, you sometimes have to walk away to show the other party, that you are not a pushover. AI consider this human behavior a bug.
However, if you zoom out and consider the optimal deal-making strategy over multiple deals, then walking away can be a good strategy. For example, a used car salesman would rationally walk away from a deal if they believed it's likely they can sell a car later for a better price.
If you consider multiple deals then you can also consider the concept of your reputation. This is information that other parties may have about you when they enter a negotiation in the future. You may rationally wish to make a sacrifice on a present deal in order to alter your reputation, to improve your outcomes in future detals.
It'd be interesting if the agents developed their own language during the reinforcement learning stage that is unintelligible to humans but allows them to quickly navigate the negotiation. They use the model trained in a supervised way during the reinforcement learning stage to avoid this, but I'm curious to see what the agent learns when paired against another reinforcement learning agent. Edit: Indeed, the paper sa…
Can we measure if the language is more efficient at getting deals done?