The Bounded Bayesian

03/13/2013
by   Kathryn Blackmond Laskey, et al.
0

The ideal Bayesian agent reasons from a global probability model, but real agents are restricted to simplified models which they know to be adequate only in restricted circumstances. Very little formal theory has been developed to help fallibly rational agents manage the process of constructing and revising small world models. The goal of this paper is to present a theoretical framework for analyzing model management approaches. For a probability forecasting problem, a search process over small world models is analyzed as an approximation to a larger-world model which the agent cannot explicitly enumerate or compute. Conditions are given under which the sequence of small-world models converges to the larger-world probabilities.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

09/04/2018

Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard

We study the Bayesian model of opinion exchange of fully rational agents...
06/20/2022

Towards Using Promises for Multi-Agent Cooperation in Goal Reasoning

Reasoning and planning for mobile robots is a challenging problem, as th...
06/15/2020

Pessimism About Unknown Unknowns Inspires Conservatism

If we could define the set of all bad outcomes, we could hard-code an ag...
04/26/2021

Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty

Reasoning about the future behavior of other agents is critical to safe ...
10/29/2019

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Much of model-based reinforcement learning involves learning a model of ...
02/23/2021

Inferring Agents Preferences as Priors for Probabilistic Goal Recognition

Recent approaches to goal recognition have leveraged planning landmarks ...
08/28/2020

Formal methods for aN Iterated volunteer's dilemma

Game theory provides a paradigm through which we can study the evolving ...