Time-off notice: no new posts for the time being

“Efficient Planning under Uncertainty with Macro-actions” by R. He, E. Brunskill and N. Roy

“Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD). PBD leverages a novel method for calculating the posterior distribution over beliefs that result after a sequence of actions is taken, given the set of observation sequences that could be received during this process. This method allows us to efficiently evaluate the expected reward of a sequence of primitive actions, which we refer to as macro-actions…”

“Multiagent Learning in Large Anonymous Games” by I. A. Kash, E. J. Friedman and J. Y. Halpern (2011)

In large systems, it is important for agents to learn to act effectively, but sophisticated multi-agent learning algorithms generally do not scale. An alternative approach is to find restricted classes of games where simple, efficient algorithms converge. It is shown that stage learning efficiently converges to Nash equilibria in large anonymous games if best-reply dynamics converge. Two features are identified that improve convergence. First, rather than making learning more difficult, more agents are actually beneficial in many settings. Second, providing agents with statistical information about the behavior of others can significantly reduce the number of observations needed.

“Iterated Belief Change Due to Actions and Observations” by A. Hunter and J. P. Delgrande (2011)

In action domains where agents may have erroneous beliefs, reasoning about the effects of actions involves reasoning about belief change. The Article presents a set of rationality properties describing the interaction between revision and update, and introduces a new class of belief change operators for reasoning about alternating sequences of revisions and updates. 

“A Probabilistic Approach for Maintaining Trust Based on Evidence” by Y. Wang, C. Hang and M. P. Singh (2011)

This paper builds on a formal model that considers probability and certainty as two dimensions of trust. It proposes a mechanism using which an agent can update the amount of trust it places in other agents on an ongoing basis