A real world environment is often partially observable by the agents either because of noisy sensors or incomplete perception. Moreover, it has continuous state space in nature, and agents must decide on an action for each point in internal continuous belief space. Consequently, it is convenient to model this type of decision-making problems as Partially Observable Markov Decision Processes (PO...

OBJECTIVES This study examines whether belief in the food addiction construct is associated with support for obesity-related policies (e.g., restrictions on foods served in schools and workplace cafeterias, subsidies on fruits and vegetables), while simultaneously examining other factors associated with policy support (e.g., political party affiliation). DESIGN Cross-sectional. SETTING Onli...

Abstract Farmers are front-line workers managing climatic change. As in many parts of the world, climate change northern California is threatening natural resource-dependent communities by exacerbating droughts, heatwaves, and wildfires. This article draws on ethnographic methods, including 108 interviews with crop livestock farmers key informants, to query experience, belief, response rural no...

for many religious people there is a problem of doubting various creedal statements contained in their religions. often propositional beliefs are looked upon as a necessary, though not sufficient, condition, for salvation. this causes great anxiety in doubters and raises the question of the importance of belief in religion and in life in general. it is a question that has been neglected in phil...

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate...