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and the new observation, O^ = {obs(n,
volume,0,1)}.
The program conf(C)
=
a(AD2, Fl) U O^ is found to be inconsistent,. So it is augmented with the diagnostic
module D.MQ. The resulting program is stiU hiconsist.ent.
The diagnostic module, DA7o will compute only tli(jse explanations in which
the unobser\ed exogenous actions occur in parallel with agent's action.
In Example 5.3, Fi predicts val(33, volume, 0,1) which contradicts the observation o6s(17. volume, 0,1).
Therefore, we add DMQ to find explanations. DMQ
will look for an exogenous action that occurs at the same time as the agent action
tutn(clo.^e).
But fails to find one.
In reality an unobserved exogenous event must have happened before the
agent action was executed. A possible explanation for the unexpected observation
o6s(17, volume, 0,1) is the unobserved occurrence of break at time 1 of step 0.
Similarly, there may arise situations in which break could have happened at
time point 0 or time point 2 of step 0. DMQ will be unable to explain the unexpected
observations in both situations.
Therefore, answer set programming alone is not enough to do diagnosis in such
situations. We have some ideas on how to approach such problems. But before we
talk about them, there are other issues such as grounding that must be addressed.
In most of our examples agent's knowledge is encoded in A-prolog and inference
engines such as SMODELS are used to reason about such knowledge. Computing
values of processes may involve trignometric functions, differential equations, complex
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