Introduction to Artificial Intelligence 2069
Attempt
all questions. . (10x6=60)
1. What do you mean by forward chaining? Why it is required? Explain it with two practical examples.
When we have some data and we make a decision based on this data then the process is called as forward chaining. Forward chaining starts with the available data and uses inference rules to extract more data until a goal is reached.
Forward chaining has the capability of providing a lot of data from the available few initial data or facts.
Forward chaining is a very popular technique for implementation to expert system, and system using production rules in the knowledge base. For expert system that needs interruption, control, monitoring and planning, the forward chaining is the best.
Examples:
1. While diagnosing a patient the doctor first check the symptoms and medical condition of the body such as temperature, blood, pressure, pulse, blood etc. After that, the patient symptoms are analysed and compared against the predetermined symptoms. Then the doctor is able to provide the medicine according to the symptoms of the patient.
2. "if it is raining then we will take umbrella". Here "it is raining" is data and "we will take umbrella" is a decision. It was alrready known that it is raining that is why we are going to take umbrella. This is forward chaining.
2. “System that think like humans” and “System that act like humans” are the part of artificial intelligence. Justify that statement with practical examples.
Artificial intelligence is about designing systems that are as intelligent as humans. It involves trying to understand human thought and an effort to build machines that emulate the human thought process.
System that think like humans
Defn: The exciting new effort to make computers think machines with minds, in the full and literal sense.
E.g. a program that think like humans, i.e. a cognitive modeling approach in which once we have sufficiently precise theory of mind , it become possible to express the theory as computer program.
System that act like humans
Defn: ”The art of creating machines that perform functions that require when performed by people” .
E.g. Turing Test approach: Which is based on the indistinguishability from undeniably intelligent entities.
You enter a room which has a computer terminal. You have a fixed period of time to type what you want into the terminal, and study the replies. At the other end of the line is either a human being or a computer system.
If it is a computer system, and at the end of the period you cannot reliably determine whether it is a system or a human, then the system is deemed to be intelligent.
3. Why normal forms are required in AI? How do you convert to the disjunctive normal form? Explain all the steps with practical examples.
A sentence that is expressed as a disjunction of conjuction of literals is said to be in disjunctive normal form (DNF). E.g. (P∧Q)∨(P∧R)
DNF conversion steps
1. Eliminate ↔ rewriting P↔Q as (P→Q)∧(Q→P)
2. Eliminate → rewriting P→Q as ¬P∨Q
3. Use De Morgan‘s laws to push ¬ inwards:
- rewrite ¬(P∧Q) as ¬P∨¬Q
- rewrite ¬(P∨Q) as ¬P∧¬Q
4. Eliminate double negations: rewrite ¬¬P as P
5. Use the distributive laws to get DNF:
- rewrite P∧(Q∨R) as (P∧Q)∨(P∧R)
6. Use:
- (P∧Q) ∧ R as P∧Q ∧ R
- (P∨Q)∨R as P∨Q∨R
4. “A deductive system is sound if any formula that can be derived in the system is logically valid. Conversely, a deductive system is complete if every logically valid formula is derivable. All of the system discussed in this article are both sound and complete. They also share the property that it is possible to effectively verify that a purportedly valid deduction is actually a deduction; such deduction systems are called effective”. Represent the above sentences in first-order logic and explain each step.
5. Justify that AI can’t exist without searching. Explain in detail about any two types of informed search with practical examples.
AI problems can be readily modeled as state spaces, where we want to find the best possible solution, that successfully solves a particular task, among all the available candidate solutions in the solution space. Using different searching algorithms we can find the best possible solution. So searching is important in AI.
Informed search uses domain-dependent (heuristic) information in order to search the space more efficiently. It uses the heuristic function h(n) that estimates how close we are to a goal. The function is used to estimate the cost from a state n to the closest goal.
Types of Informed Search:
- Greedy Best-first Search
- A* Search
In above given search, nodes are expanded based on the value of evaluation function. Nodes having minimal value of evaluation function are expanded first.
Greedy Best-first Search
- It expands the node that appears to be closest to the goal.
- The evaluation function is defined by f(n) = h(n)
Where, h(n) is an estimate of cost from node n to goal node
h(n) = 0 for goal node.
A* Search
The evaluation function is defined by f(n) = g(n) + h(n)
Where, g(n) is sum of actual costs incurred while travelling from root node (start node) to node n.
h(n) is an estimate of cost from node n to goal node
f(n) is estimated total cost of path through n to goal.
6. Why do we require learning? Explain about learning framework with suitable block diagram and examples.
Learning is acquiring new or modifying existing knowledge, behaviors, skills and may involve synthesizing different types of information.
We require machine learning because of the following reasons:
- Huge computational power is available
- To understand and improve efficiency of human learning.
- To discover new things or structure that is unknown to humans.
- To fill in sketal or incomplete specification about a domain.
Learning Framework:
It consists of the following components:
- Learning element: This element is responsible for making improvements.
- Performance element: It is responsible for selecting external actions according to the percepts it takes.
- Critic: It provides feedback to the learning agent about how well the agent is doing, which could maximize the performance measure in the future.
- Problem Generator: It suggests actions which could lead to new and informative experiences.
Example:
Performance element: turning, accelerating, breaking are the performance elements on road.
Learning element: learn rules for breaking, accelerating, learn geography of the city.
Critic: quick right turn across three lanes of traffic, observes reaction of other drivers.
Problem generator: Try south city road.
7. What do you mean by casual network? Explain it with practical application.
Causal Networks / Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.
- Causal Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
- Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
- The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
- Each nodes are labelled with probability.
E.g.
P(x) = 0.5
P(y/x) = 0.7
P(z) = 0.8
P(u/y) = 0.47
Applications of Causal / Bayesian Network:
1. Spam Filter: You must be lying if you say that you’ve never wondered how Gmail filters spam emails (unwanted and unsolicited emails. It uses Bayesian spam filter, which is the most robust filter.
2. Turbo Code: Bayesian Networks are used to create turbo codes that are high-performance forward error correction codes. These are used in 3G and 4G mobile networks.
3. Image Processing: Bayesian Networks use mathematical operations to convert images into digital format. It also allows image enhancement.
4. Biomonitoring: Quantifying the concentration of chemicals couldn’t get any easier than with Bayesian Networks. In this, the amount of blood and tissue in humans is measured using indicators.
5. Gene Regulatory Network (GNR): A GNR contains various DNA segments of a cell that interact with other cell contents through protein and RNA expression products. The predictions of its behavior can be analyzed using Bayesian Networks.
8. What is a Neural Network? Explain any one type of neural network with practical example.
- Neural Networks are networks of neurons, for example, as
found in real (i.e. biological) brains.
- Artificial neurons are crude approximations of the neurons found
in real brains. They may be physical devices, or purely mathematical
constructs.
- Artificial Neural Networks (ANNs) are networks of Artificial Neurons and hence constitute crude approximations to parts of real brains. They maybe physical devices, or simulated on conventional.
- Computers point of view, an ANN is just a parallel computational system consisting of many simple processing elements connected together in a specific way in order to perform a particular task.
An artificial neural network is a composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problem. ANN has a number of input channels, a processing stages and output.
Feed-forward Neural Network
Feed-forward ANNs allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition.
9. Knowledge consists of facts, beliefs, and heuristics, justify it. Explain the advantages and disadvantages of an expert system.
Knowledge is a theoretical or practical understanding of a subject or a domain. Knowledge is also the sum of what is currently known. Knowledge consists of information that has been:
– interpreted,
– categorised,
– applied, experienced and revised.
So knowledge consists of: facts, ideas, beliefs, heuristics, associations, rules, abstractions, relationships, customs.
An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.
Advantages of Expert System
- It provides consistent answer for repetitive decisions, processes and tasks.
- It holds and maintain levels of information.
- It can tackle very complex problems that are difficult for human expert to solve.
- It improves the decision quality.
- It reduces the cost of consulting experts for problem solving.
- It provides quick and efficient solutions to problem in narrow area of specialization.
- It can discover new knowledge.
Disadvantages of Expert System
- It lacks common sense needed in decision making.
- The knowledge base may not be complete.
- Expensive to build & maintain.
- Takes long time to develop.
- Errors in the knowledge base can lead to wrong decisions.
- Unable to make creative response in an extraordinary situations.
10. Differentiate between natural language understanding (NLU) and natural language generating (NLG). Why we have to study natural language processing? Explain it.
Natural Language Understanding(NLU): It is the process of mapping the given inputs in natural language into useful representation and analyzing different aspects of the language.
Natural Language Generation(NLG): It is the process of producing meaningful phrase and sentences in the form a machine representation system such as KB or logical form.
Importance of NLP:
- NLP helps to make communication & handling easy between the user and computer system.
- Helps to understand large social dat available in the internet.
- Improve the efficiency and accuracy of documentation and identify the most pertient information from large database.