AI Chat AI Image Generator AI Video Text to Speech

Distribution over Beliefs for Memory Bounded Dec-POMDP Planning

03/15/2012

∙

by Gabriel Corona, et al.

∙

∙

We propose a new point-based method for approximate planning in Dec-POMDP which outperforms the state-of-the-art approaches in terms of solution quality. It uses a heuristic estimation of the prior probability of beliefs to choose a bounded number of policy trees: this choice is formulated as a combinatorial optimisation problem minimising the error induced by pruning.

Gabriel Corona
1 publication
François Charpillet
8 publications

research

∙ 02/21/2016

Social planning for social HRI

Making a computational agent 'social' has implications for how it percei...

0 Liz Sonenberg, et al. ∙

research

∙ 07/10/2019

An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types

Many multiagent applications require an agent to learn quickly how to in...

5 Stefano V. Albrecht, et al. ∙

research

∙ 05/10/2019

Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling

State-of-the-art approaches to partially observable planning like POMCP ...

0 Thomy Phan, et al. ∙

research

∙ 07/11/2019

Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning

We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a gene...

0 Thomy Phan, et al. ∙

research

∙ 02/18/2021

A maximum entropy model of bounded rational decision-making with prior beliefs and market feedback

Bounded rationality is an important consideration stemming from the fact...

0 Benjamin Patrick Evans, et al. ∙

research

∙ 09/04/2022

Interpreting systems as solving POMDPs: a step towards a formal understanding of agency

Under what circumstances can a system be said to have beliefs and goals,...

0 Martin Biehl, et al. ∙

research

∙ 08/28/2018

Evaluating Theory of Mind in Question Answering

We propose a new dataset for evaluating question answering models with r...

0 Aida Nematzadeh, et al. ∙