Dempster Shafer Theory AI CSE 8th Sem

1
Topic 4
Representation and Reasoning
with Uncertainty
Contents
4.0 Representing Uncertainty
4.1 Probabilistic methods
4.2 Certainty Factors (CFs)
4.3 Dempster-Shafer theory
4.4 Fuzzy Logic
4.3 Dempster-Shafer Theory
• Dempster-Shafer theory is an approach to combining
evidence
• Dempster (1967) developed means for combining
degrees of belief derived from independent items of
evidence.
• His student, Glenn Shafer (1976), developed method
for obtaining degrees of belief for one question from
subjective probabilities for a related question
• People working in Expert Systems in the 1980s saw
their approach as ideally suitable for such systems.

2
• Each fact has a degree of support, between 0 and 1:
– 0 No support for the fact
– 1 full support for the fact
• Differs from Bayesian approah in that:
– Belief in a fact and its negation need not sum to 1.
– Both values can be 0 (meaning no evidence for or against the
fact)
Set of possible conclusions: Θ
Θ = { θ1, θ2, …, θn}
Where:
– Θ is the set of possible conclusions to be drawn
– Each θi is mutually exclusive: at most one has to be
true.
– Θ is Exhaustive: At least one θi has to be true.

3
Frame of discernment :
Θ = { θ1, θ2, …, θn}
• Bayes was concerned with evidence that supported single
conclusions (e.g., evidence for each outcome θi in Θ):
• p(θi | E)
• D-S Theoryis concerned with evidences which support
subsets of outcomes in Θ, e.g.,
θ1 v θ2 v θ3 or {θ1, θ2, θ3}
Frame of discernment :
• The “frame of discernment” (or “Power set”) of Θ is the set
of all possible subsets of Θ:
– E.g., if Θ = { θ1, θ2, θ3}
• Then the frame of discernment of Θ is:
( Ø, θ1, θ2, θ3, {θ1, θ2}, {θ1, θ3}, {θ2, θ3}, { θ1, θ2, θ3} )
• Ø, the empty set, has a probability of 0, since one of the
outcomes has to be true.
• Each of the other elements in the power set has a
probability between 0 and 1.
• The probability of { θ1, θ2, θ3} is 1.0 since one has to be
true.

4
Mass function m(A):
(where A is a member of the power set)
= proportion of all evidence that supports this element of
the power set.
“The mass m(A) of a given member of the power set, A,
expresses the proportion of all relevant and available
evidence that supports the claim that the actual state
belongs to A but to no particular subset of A.” (wikipedia)
“The value of m(A) pertains only to the set A and makes no
additional claims about any subsets of A, each of which
has, by definition, its own mass.
Mass function m(A):
• Each m(A) is between 0 and 1.
• All m(A) sum to 1.
• m(Ø) is 0 - at least one must be true.

5
Mass function m(A): Interpetation of m({AvB})=0.3
• means there is evidence for {AvB} that cannot be
divided among more speciﬁc beliefs for A or B.
Mass function m(A): example
• 4 people (B, J, S and K) are locked in a room when the
lights go out.
• When the lights come on, K is dead, stabbed with a knife.
• Not suicide (stabbed in the back)
• No-one entered the room.
• Assume only one killer.
• Θ = { B, J, S}
• P(Θ) = (Ø, {B}, {J}, {S}, {B,J}, {B,S}, {J,S}, {B,J,S} )

6
Mass function m(A): example (cont.)
• Detectives, after reviewing the crime-scene, assign mass
probabilities to various elements of the power set:
0No-one is guilty
0.1One of the 3 is guilty
0.3either S or J is guilty
0.1either B or S is guilty
0.1either B or J is guilty
0.1S is guilty
0.2J is guilty
0.1B is guilty
MassEvent
Belief in A:
The belief in an element A of the Power set is the sum of
the masses of elements which are subsets of A (including
A itself).
E.g., given A={q1, q2, q3}
Bel(A) = m(q1)+m(q2)+m(q3)
+ m({q1, q2})+m({q2, q3})+m({q1, q3})
+m({q1, q2, q3})

7
Belief in A: example
• Given the mass assignments as assigned by the
detectives:
• bel({B}) = m({B}) = 0.1
• bel({B,J}) = m({B})+m({J})+m({B,J}) =0.1+0.2+0.1=0.4
• Result:
0.3
{J,S}
0.10.10.10.10.20.1m(A)
{B,J,S}{B,S}{B,J}{S}{J}{B}A
1.00.60.30.40.10.20.1bel(A)
0.3
{J,S}
0.10.10.10.10.20.1m(A)
Plausibility of A: pl(A)
The plausability of an element A, pl(A), is the sum of
all the masses of the sets that intersect with the set A:
E.g. pl({B,J}) = m(B)+m(J)+m(B,J)+m(B,S)
+m(J,S)+m(B,J,S)
= 0.9
1.00.90.80.90.60.70.4pl(A)
0.3
{J,S}
0.10.10.10.10.20.1m(A)
All Results:

8
Disbelief (or Doubt) in A: dis(A)
The disbelief in A is simply bel(¬A).
It is calculated by summing all masses of elements which do
not intersect with A.
The plausibility of A is thus 1-dis(A):
pl(A) = 1- dis(A)
00.10.20.10.40.30.6dis(A)
1.00.90.80.90.60.70.4pl(A)
0.3
{J,S}
0.10.10.10.10.20.1m(A)
Belief Interval of A:
The certainty associated with a given subset A is defined by the
belief interval:
[ bel(A) pl(A) ]
E.g. the belief interval of {B,S} is: [0.1 0.8]
1.00.60.30.40.10.20.1bel(A)
1.00.90.80.90.60.70.4pl(A)
0.3
{J,S}
0.10.10.10.10.20.1m(A)

9
Belief Intervals & Probability
The probability in A falls somewhere between bel(A) and
pl(A).
– bel(A) represents the evidence we have for A directly.
So prob(A) cannot be less than this value.
– pl(A) represents the maximum share of the evidence we
could possibly have, if, for all sets that intersect with A,
the part that intersects is actually valid. So pl(A) is the
maximum possible value of prob(A).
1.00.60.30.40.10.20.1bel(A)
1.00.90.80.90.60.70.4pl(A)
0.3
{J,S}
0.10.10.10.10.20.1m(A)
Belief Intervals:
Belief intervals allow Demspter-Shafer theory to reason
about the degree of certainty or certainty of our beliefs.
– A small difference between belief and plausibility shows
that we are certain about our belief.
– A large difference shows that we are uncertain about
our belief.
• However, even with a 0 interval, this does not mean we
know which conclusion is right. Just how probable it is!
1.00.60.30.40.10.20.1bel(A)
1.00.90.80.90.60.70.4pl(A)
0.3
{J,S}
0.10.10.10.10.20.1m(A)

Dempster Shafer Theory AI CSE 8th Sem

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Dempster Shafer Theory AI CSE 8th Sem