I propose a belief maintenance framework which draws from partially observable Markov decision processes (POMDPs) [1] and my hybrid stochastic belief change (HSBC) construction [2] to accommodate a stream of observations. Agent actions and environment events are distinguishable and form part of the agent model. It is left up to the agent designer to provide an environment model; a submodel of the agent model. Observations in the stream which are no longer relevant, become default assumptions until overridden by newer, more prevalent observations. A distinction is made between background and foreground beliefs. Voorbraak's [3] partial probability theory (PPT) is used as guidance for the 'dual-belief-base' approach. In PPT, the agent may be somewhat ignorant, or might not have all (probabilistic) knowledge desired. Consider the following scenario. Your neighbour tells you he needs to visit the dentist urgently. You know that he uses the dentist at the Wonder-mall. A month later, you see your neighbour at the Wonder-mall. Is he there to see the dentist? The answer to the question makes use of the persistence of truth of certain pieces of information. After a period has elapsed, the veracity of some kinds of information dissipates. One would expect a person who says they must visit the dentist urgently to visit the dentist within approximately seven days. So your neighbour is probably not at Wonder-mall to see the dentist. Hence 'Neighbour must visit dentist' should be true for no longer than seven days, after which, the statement becomes defeasibly true.
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