CC–13 : Artificial Intelligence (UNIT–4)
Dealing with Uncertainty and Inconsistencies: Till now, we have learnt
knowledge representation using first-order logic and propositional logic with
certainty, which means we were sure about the predicates. With this knowledge
representation, we might write A→B, which means if A is true then B is true, but
consider a situation where we are not sure about whether A is true or not then we
cannot express this statement, this situation is called uncertainty.
So to represent uncertain knowledge, where we are not sure about the predicates,
we need uncertain reasoning or probabilistic reasoning.
Causes of uncertainty: Following are some leading causes of uncertainty to occur
in the real world.
1. Information occurred from unreliable sources.
2. Experimental Errors
3. Equipment fault
4. Temperature variation
5. Climate change.
Truth maintenance system: A truth maintenance system, or TMS, is
a knowledge representation method for representing both beliefs and their
dependencies and an algorithm called the "truth maintenance algorithm" that
manipulates and maintains the dependencies.
A truth maintenance system maintains consistency between old believed knowledge
and current believed knowledge in the knowledge base (KB) through revision. If the
current believed statements contradict the knowledge in the KB, then the KB is
updated with the new knowledge. It may happen that the same data will again be
believed, and the previous knowledge will be required in the KB.
Each statement having at least one valid justification is made a part of the current
belief set. When a contradiction is found, the statement(s) responsible for the
contradiction are identified and the records are appropriately updated. This process
is called dependency-directed backtracking.
The TMS algorithm maintains the records in the form of a dependency network.
Each node in the network is an entry in the KB (a premise, antecedent, or inference
rule etc.) Each arc of the network represents the inference steps through which the
node was derived.
A premise is a fundamental belief which is assumed to be true. They do not need
justifications. The set of premises are the basis from which justifications for all other
nodes will be derived.
Reasoning: The reasoning is the mental process of deriving logical conclusion
and making predictions from available knowledge, facts, and beliefs. Or we can say,
"Reasoning is a way to infer facts from existing data." It is a general process of
thinking rationally, to find valid conclusions. In artificial intelligence, reasoning is
essential so that the machine can also think rationally as a human brain, and can
perform like a human.
Manas Ku Mishra, Asst. Prof. of Comp. Sc., FM (A) College, BLS. Page 1 of 9
, Types of Reasoning: In artificial intelligence, reasoning can be divided into the
following categories:
Deductive reasoning
Inductive reasoning
Abductive reasoning
Common Sense Reasoning
Monotonic Reasoning
Non-monotonic Reasoning
1. Deductive reasoning: Deductive reasoning is deducing new information from
logically related known information. It is the form of valid reasoning, which means
the argument's conclusion must be true when the premises are true. It is sometimes
referred to as top-down reasoning.
Example: Premise-1: All the humans eat veggies
Premise-2: Suresh is human.
Conclusion: Suresh eats veggies.
The general process of deductive reasoning is given below:
Theory Hypothesis Patterns Confirmation
2. Inductive Reasoning: Inductive reasoning is a form of reasoning to arrive at a
conclusion using limited sets of facts by the process of generalization. It starts with
the series of specific facts or data and reaches to a general statement or conclusion.
Inductive reasoning is a type of propositional logic, which is also known as cause-
effect reasoning or bottom-up reasoning.
In inductive reasoning, we use historical data or various premises to generate a
generic rule, for which premises support the conclusion. Premises provide probable
supports to the conclusion, so the truth of premises does not guarantee the truth of
the conclusion.
Example: Premise: All of the pigeons we have seen in the zoo are white.
Conclusion: Therefore, we can expect all the pigeons to be white.
Observations patterns Hypothesis Theory
3. Abductive reasoning: Abductive reasoning is a form of logical reasoning which
starts with single or multiple observations then seeks to find the most likely
explanation or conclusion for the observation. It is an extension of deductive
reasoning, but in abductive reasoning, the premises do not guarantee the
conclusion.
Example: Implication: Cricket ground is wet if it is raining
Axiom: Cricket ground is wet.
Conclusion: It is raining.
4. Common Sense Reasoning: Common sense reasoning is an informal form of
reasoning, which can be gained through experiences. It simulates the human ability
Manas Ku Mishra, Asst. Prof. of Comp. Sc., FM (A) College, BLS. Page 2 of 9
Dealing with Uncertainty and Inconsistencies: Till now, we have learnt
knowledge representation using first-order logic and propositional logic with
certainty, which means we were sure about the predicates. With this knowledge
representation, we might write A→B, which means if A is true then B is true, but
consider a situation where we are not sure about whether A is true or not then we
cannot express this statement, this situation is called uncertainty.
So to represent uncertain knowledge, where we are not sure about the predicates,
we need uncertain reasoning or probabilistic reasoning.
Causes of uncertainty: Following are some leading causes of uncertainty to occur
in the real world.
1. Information occurred from unreliable sources.
2. Experimental Errors
3. Equipment fault
4. Temperature variation
5. Climate change.
Truth maintenance system: A truth maintenance system, or TMS, is
a knowledge representation method for representing both beliefs and their
dependencies and an algorithm called the "truth maintenance algorithm" that
manipulates and maintains the dependencies.
A truth maintenance system maintains consistency between old believed knowledge
and current believed knowledge in the knowledge base (KB) through revision. If the
current believed statements contradict the knowledge in the KB, then the KB is
updated with the new knowledge. It may happen that the same data will again be
believed, and the previous knowledge will be required in the KB.
Each statement having at least one valid justification is made a part of the current
belief set. When a contradiction is found, the statement(s) responsible for the
contradiction are identified and the records are appropriately updated. This process
is called dependency-directed backtracking.
The TMS algorithm maintains the records in the form of a dependency network.
Each node in the network is an entry in the KB (a premise, antecedent, or inference
rule etc.) Each arc of the network represents the inference steps through which the
node was derived.
A premise is a fundamental belief which is assumed to be true. They do not need
justifications. The set of premises are the basis from which justifications for all other
nodes will be derived.
Reasoning: The reasoning is the mental process of deriving logical conclusion
and making predictions from available knowledge, facts, and beliefs. Or we can say,
"Reasoning is a way to infer facts from existing data." It is a general process of
thinking rationally, to find valid conclusions. In artificial intelligence, reasoning is
essential so that the machine can also think rationally as a human brain, and can
perform like a human.
Manas Ku Mishra, Asst. Prof. of Comp. Sc., FM (A) College, BLS. Page 1 of 9
, Types of Reasoning: In artificial intelligence, reasoning can be divided into the
following categories:
Deductive reasoning
Inductive reasoning
Abductive reasoning
Common Sense Reasoning
Monotonic Reasoning
Non-monotonic Reasoning
1. Deductive reasoning: Deductive reasoning is deducing new information from
logically related known information. It is the form of valid reasoning, which means
the argument's conclusion must be true when the premises are true. It is sometimes
referred to as top-down reasoning.
Example: Premise-1: All the humans eat veggies
Premise-2: Suresh is human.
Conclusion: Suresh eats veggies.
The general process of deductive reasoning is given below:
Theory Hypothesis Patterns Confirmation
2. Inductive Reasoning: Inductive reasoning is a form of reasoning to arrive at a
conclusion using limited sets of facts by the process of generalization. It starts with
the series of specific facts or data and reaches to a general statement or conclusion.
Inductive reasoning is a type of propositional logic, which is also known as cause-
effect reasoning or bottom-up reasoning.
In inductive reasoning, we use historical data or various premises to generate a
generic rule, for which premises support the conclusion. Premises provide probable
supports to the conclusion, so the truth of premises does not guarantee the truth of
the conclusion.
Example: Premise: All of the pigeons we have seen in the zoo are white.
Conclusion: Therefore, we can expect all the pigeons to be white.
Observations patterns Hypothesis Theory
3. Abductive reasoning: Abductive reasoning is a form of logical reasoning which
starts with single or multiple observations then seeks to find the most likely
explanation or conclusion for the observation. It is an extension of deductive
reasoning, but in abductive reasoning, the premises do not guarantee the
conclusion.
Example: Implication: Cricket ground is wet if it is raining
Axiom: Cricket ground is wet.
Conclusion: It is raining.
4. Common Sense Reasoning: Common sense reasoning is an informal form of
reasoning, which can be gained through experiences. It simulates the human ability
Manas Ku Mishra, Asst. Prof. of Comp. Sc., FM (A) College, BLS. Page 2 of 9
