Some definitions/pioneering thoughts on Artificial Intelligence
In Computer Science, work termed “AI” has traditionally focused on the high-level problem; on imparting high-level abilities to “use language, form abstractions and concepts” and to “solve kinds of problems now reserved for humans” (McCarthy et al. 1955)
AI is the science of making computers do things that require intelligence like humans (Minsky)
The automation of activities that we associate with human thinking, activities such as decision making, problem solving, learning … (Bellman, 1978)
Physicists ask what kind of place this Universe is and seek to characterize its behavior systematically. Biologists ask what it means for a physical system to be living. We (in AI) wonder what kind of information processing system can ask such questions – Avron Barr and Edward Feigenbaum (1981)
The fundamental goal of this research is not merely to mimic intelligence or produce some clever fake. “AI” wants the genuine article; machines with minds – John Haugeland (1985)
AI is the study of mental faculties through the use of computational models – Eugene Charnaik and Drew McDermott (1985)
We call programs ‘intelligent’ if they exhibit behaviors that would be regarded intelligent if they were exhibited by human beings – Herbert Simon
AI is the study of techniques for solving exponentially hard problems in polynomial time by exploiting knowledge about the problem domain – Elaine Rich
The art of creating machines that perform functions that require intelligence when performed by people (Kurzweil, 1990)
The study of the computation that make it possible to perceive, reason and act (Winston, 1992)
AI … is concerned with intelligent behavior in artifacts (Nilsson, 1998)
Computational Intelligence is the study of the design of intelligent agents (Poole et al., 1998)
AI is a branch of computer science concerned with the study and creation of computer systems that exhibit some form of intelligence: systems that learn new concepts and tasks, systems that can reason and draw useful conclusions about the world around us, systems that can understand a natural language or perceive and comprehend a visual scene, and systems that perform other types of feats that require human types of intelligence (Dan W. Patterson)
1. Deepak Khemani, A first course in AI, Mcgraw Hill Education
2. Stuart Russell and Peter Norvig, AI: a modern approach, Second Edition, Pearson
4. Dan W. Patterson, Introduction to Artificial Intelligence and Expert Systems, PHI ISBN-978-81-203-0777-3
Q. What are Knowledge Based Systems?
Knowledge Based systems are systems that contain a good amount of knowledge to perform difficult tasks. In his seminal 1977 paper at the Joint International Conference on Artificial Intelligence (IJCAI), Edward Feigenbaum emphasized that the real power of an expert system comes from the knowledge it possesses rather than the particular inference schemes and other formalisms it employs .
Much of the work in AI has been related to Knowledge Based systems, which includes work in Expert Systems, Natural Language Understanding, Vision and others.
Knowledge based systems derive their power from the knowledge base. The knowledge base is a repository of facts, rules, heuristics and procedures. The knowledge base is segregated from the control and inferencing components enabling it to add additional knowledge or refine existing knowledge independently.
1. Dan W. Patterson, Introduction to AI and Expert Systems, PHI, ISBN-978-81-203-0777-3
Q. What is Knowledge Engineering?
Knowledge Engineering is a skill-set expected in a Knowledge Engineer. A knowledge engineer is responsible for designing and building a KBS (Knowledge based systems). For this, a knowledge engineer has to interact with human experts (domain knowledge) and incorporate the knowledge using Knowledge Representation schemes (rules, frames & others) into a knowledge base. Once appropriate knowledge have been encoded, a KBS system can be useful for discovery of knowledge to be made use of in real-life applications such as Robotics, Tele Medicine or in Traffic Control.
The success of Knowledge Engineering would depend on the ability of the Knowledge Engineer to extract expert knowledge from the minds of human experts and other informative sources from relevant domains.
The AI scientist Edward Feigenbaum describes the knowledge engineer in the following words “The Knowledge Engineer practices the art of bringing the principles and tools of AI research to bear on difficult application problems requiring experts’ knowledge for their solution. The technical issues of acquiring this knowledge, representing it, and using it appropriately to construct and explain lines-of-reasoning, are important problems in the design of knowledge-based systems … The art of constructing intelligent agents is both a part of and an extension of the programming art. It is the art of building complex computer programs that represent and reason with knowledge of the world (Feigenbaum, 1977, p. 1015)” 
1. George M. Marakas, Decision Support Systems in the 21st Century, PHI ISBN-978-81-203-2376-6
Q. What is a Heuristic? Justify how heuristics can be effective to solve problems?
A heuristic is an intelligent guess used in problem solving. It is an approximation done to reduce the search space. A heuristic function defines a state in terms of a number and this number is used for decision making in the search problems. Heuristics are employed when
Ø The perfect solution to the problem is not known
Ø The best solution is not computationally feasible
Example – In a best first search algorithm the heuristic used is the distance of the node from the goal state. In the 8 tile problem, hamming distance and Manhattan distance are used as heuristics
Effectiveness of heuristics – Heuristics are effective to solve search problems because they drastically reduce the search space because each time the alternatives are explored one out of them is chosen which is further expanded, so we do not have to traverse all nodes.