Managing quality in agent dialogues
Measuring the quality of answers in agent’s conversations. Quality measures:
precision (smaller interval) and certainly (how close is to true or false). They are related: precision is more interesting when certainly is close to true or false. Talking about absolute (values for the facts) and relative (external view) quality. But explaining all these thing he’s run out of time, so he can’t explain how agents can use this quality measures.
Designing Automated Agents Capable of Efficiently Negotiating with People – Overcoming the Challenge
It’s very difficult to design domain independent agents that negotiate with other agents or with people, and this is the goal of the paper. HE establihes the negotiation environment, how the agent isdesigned and shows some samples in games andother environments: Diplomacy, autONA, Cliff-Edge… finishing with the KBAgent, which includes all the characteristics developed in the previous ones
- Generic agent / domain independent
- Qualitative decision making
- Non deterministic behavior / randomization
- Incorporating data from past interactions
And now something about validation. It is a problem because it is no standard fto do that. What is a ‘good’ agent? maximal payoff/maximal social welfare/end with agreement/pass Turing test? This is an open question.
A good question from Ingrid Nunes abuot emotions, because their influence in human negotiations. But at the moment they are not taken into account