3,173 research outputs found
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Learning strategies for imperfect information games from samples of
interaction is a challenging problem. A common method for this setting, Monte
Carlo Counterfactual Regret Minimization (MCCFR), can have slow long-term
convergence rates due to high variance. In this paper, we introduce a variance
reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR.
Using this technique, per-iteration estimated values and updates are
reformulated as a function of sampled values and state-action baselines,
similar to their use in policy gradient reinforcement learning. The new
formulation allows estimates to be bootstrapped from other estimates within the
same episode, propagating the benefits of baselines along the sampled
trajectory; the estimates remain unbiased even when bootstrapping from other
estimates. Finally, we show that given a perfect baseline, the variance of the
value estimates can be reduced to zero. Experimental evaluation shows that
VR-MCCFR brings an order of magnitude speedup, while the empirical variance
decreases by three orders of magnitude. The decreased variance allows for the
first time CFR+ to be used with sampling, increasing the speedup to two orders
of magnitude
Trust models in ubiquitous computing
We recapture some of the arguments for trust-based technologies in ubiquitous computing, followed by a brief survey of some of the models of trust that have been introduced in this respect. Based on this, we argue for the need of more formal and foundational trust models
What Do You Care About: Inferring Values from Emotions
Observers can glean information from others' emotional expressions through
the act of drawing inferences from another individual's emotional expressions.
It is important for socially aware artificial systems to be capable of doing
that as it can facilitate social interaction among agents, and is particularly
important in human-robot interaction for supporting a more personalized
treatment of users. In this short paper, we propose a methodology for
developing a formal model that allows agents to infer another agent's values
from her emotion expressions
\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay
09121 Abstracts Collection -- Normative Multi-Agent Systems
From 15.03. to 20.03.2009, the Dagstuhl Seminar 09121 ``Normative Multi-Agent Systems \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general
- ā¦