Propositions and Inference in VS .NET Creator PDF 417 in VS .NET Propositions and Inference

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5. Propositions and Inference using .net framework togenerate qr code for web,windows application Developing with Visual Studio .NET There are s .NET Denso QR Bar Code ix minimal explanations of dark l1 lit l2 :. {broken l1 , live outside, ok cb1 , ok l2 , ok s3 , up s3 } {broken s2 , live outside, ok cb1 , ok l2 , ok s3 , up s3 } {down s1 , live outside, ok cb1 , ok l2 , ok s3 , up s2 , up s3 } {broken s1 , live outside, ok cb1 , ok l2 , ok s3 , up s2 , up s3 } {down s2 , live outside, ok cb1 , ok l2 , ok s3 , up s1 , up s3 } {broken s1 , down s2 , live outside, ok cb1 , ok l2 , ok s3 , up s3 }. Notice how visual .net qrcode the explanations cannot include outside power down or broken cb1 because they are inconsistent with the explanation of l2 being lit..

The bottom- up and top-down implementations for assumption-based reasoning with Horn clauses (page 190) can both be used for abduction. The bottom-up implementation of Figure 5.9 (page 190) computes, in C, the minimal explanations for each atom.

The pruning discussed in the text can also be used. The top-down implementation can be used to nd the explanations of any g by generating the con icts and, using the same code and knowledge base, proving g instead of false. The minimal explanations of g are the minimal sets of assumables collected to prove g that are not subsets of con icts.

. Causal Models A primitive atom is an atom that is stated as an atomic clause when it is true. A derived atom is one that uses rules to de ne when it is true. Typically the designer writes axioms for the derived atoms and then expects a user to specify which primitive atoms are true.

Thus, the derived atoms will be inferred as necessary from the primitive atoms and other atoms that can be derived. The designer of an agent must make many decisions when designing a knowledge base for a domain. For example, consider two propositions, a and b, both of which are true.

There are many choices of how to write this. A designer could specify both a and b as atomic clauses, treating both as primitive. A designer could have a as primitive and b as derived, stating a as an atomic clause and giving the rule b a.

Alternatively, the designer could specify the atomic clause b and the rule a b, treating b as primitive and a as derived. These representations are logically equivalent; they cannot be distinguished logically. However, they have different effects when the knowledge base is changed.

Suppose a was no longer true for some reason. In the rst and third representations, b would still be true, and in the second representation b would no longer true. A causal model, or a model of causality, is a representation of a domain that predicts the results of interventions.

An intervention is an action that forces a variable to have a particular value; that is, it changes the value in some way other than manipulating other variables in the model.. 5.7. Causal Models To predict the effect of interventions, a causal model represents how the cause implies its effect. When the cause is changed, its effect should be changed. An evidential model represents a domain in the other direction from effect to cause.

Note that we do not assume that there is the cause of an effect; rather there are many propositions, which together make the effect true. Example 5.32 Consider the electrical domain depicted in Figure 1.

8 (page 34). In this domain, switch s3 is up and light l2 is lit. There are many different ways to axiomatize this domain.

Example 5.5 (page 164) contains causal rules such as. lit l2 up Denso QR Bar Code for .NET s3 live w3 . Alternatively, we could specify in the evidential direction: up s3 lit l2 .

live w3 lit l2 . These are all statements that are true of the domain. Suppose that wire w3 was live and someone put switch s3 up; we would expect that l2 would become lit.

However, if someone was to make s3 lit by some mechanism outside of the model (and not by ipping the switch), we would not expect the switch to go up as a side effect.. Example 5.3 3 Consider the electrical domain depicted in Figure 1.8 (page 34).

The following proposition describes an invariant on the relationship between switches s1 and s2 and light l1 , assuming all components are working properly:. up s1 (li qr bidimensional barcode for .NET t l1 up s2 ). (5.

1) This formula is symmetric between the three propositions; it is true if and only if an odd number of the propositions are true. However, in the world, the relationship between these propositions is not symmetric. Suppose all three atoms were true in some state.

Putting s1 down does not make s2 go down to preserve lit l1 . Instead, putting s1 down makes lit l1 false, and up s2 remains true to preserve this invariant. Thus, to predict the result of interventions, we require more than proposition (5.

1) above. A causal model is lit l1 up s1 up s2 . lit l1 up s1 up s2 .

The completion of this is equivalent to proposition (5.1); however, it makes reasonable predictions when one of the values is changed. An evidential model is up s1 lit l1 up s2 .

up s1 lit l1 up s2 . This can be used to answer questions about whether s1 is up based on the position of s2 and whether l1 is lit. Its completion is also equivalent to formula (5.

1). However, it does not accurately predict the effect of interventions..

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