2 research outputs found
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans
In order to collaborate safely and efficiently, robots need to anticipate how
their human partners will behave. Some of today's robots model humans as if
they were also robots, and assume users are always optimal. Other robots
account for human limitations, and relax this assumption so that the human is
noisily rational. Both of these models make sense when the human receives
deterministic rewards: i.e., gaining either 130 with certainty. But in
real world scenarios, rewards are rarely deterministic. Instead, we must make
choices subject to risk and uncertainty--and in these settings, humans exhibit
a cognitive bias towards suboptimal behavior. For example, when deciding
between gaining 130 only 80% of the time, people tend
to make the risk-averse choice--even though it leads to a lower expected gain!
In this paper, we adopt a well-known Risk-Aware human model from behavioral
economics called Cumulative Prospect Theory and enable robots to leverage this
model during human-robot interaction (HRI). In our user studies, we offer
supporting evidence that the Risk-Aware model more accurately predicts
suboptimal human behavior. We find that this increased modeling accuracy
results in safer and more efficient human-robot collaboration. Overall, we
extend existing rational human models so that collaborative robots can
anticipate and plan around suboptimal human behavior during HRI.Comment: ACM/IEEE International Conference on Human-Robot Interactio
Learning models of sequential decision-making with partial specification of agent behavior
Artificial agents that interact with other (human or artificial) agents require models in order to reason about those other agents’ behavior. In addition to the predictive utility of these models, maintaining a model that is aligned with an agent’s true generative model of behavior is critical for effective human-agent interaction. In applications wherein observations and partial specification of the agent’s behavior are available, achieving model alignment is challenging for a variety of reasons. For one, the agent’s decision factors are often not completely known; further, prior approaches that rely upon observations of agents’ behavior alone can fail to recover the true model, since multiple models can explain observed behavior equally well. To achieve better model alignment, we provide a novel approach capable of learning aligned models that conform to partial knowledge of the agent’s behavior. Central to our approach are a factored model of behavior (AMM), along with Bayesian nonparametric priors, and an inference approach capable of incorporating partial specifications as constraints for model learning. We evaluate our approach in experiments and demonstrate improvements in metrics of model alignment