Artificial Intelligence Lecture 2:Knowledge Representation I Faculty of Mathematical Sciences 4 th 5 th IT Elmuntasir Abdallah Hag Eltom

Lecture Objectives [Part I: Chapter 2] 1.Look at some of the arguments against strong AI (the belief that a computer is capable of having mental states). 2.Look at the prevalence of Artificial Intelligence today and explain why it has become such a vital area of study. 3.Look at the extent to which the Artificial Intelligence community has been successful so far in achieving the goals that were believed to be possible decades ago. In particular, we will look at whether the computer HAL in the science fiction film 2001: A Space Odyssey is a possibility with today’s technologies.

Lecture Objectives [Part II: Chapter 3] Discuss representations. The reason for this is that in order for a computer to solve a problem that relates to the real world, it first needs some way to represent the real world internally. In dealing with that internal representation, the computer is then able to solve problems. Introduce a number of representations, such as semantic nets, goal trees, and search trees. Explains why these representations provide such a powerful way to solve a wide range of problems. Introduce frames and the way in which inheritance can be used to provide a powerful representational system.

“The limits of my language mean the limits of my world.” -Ludwig Wittgenstein. The Chinese Room The American philosopher John Searle has argued strongly against the proponents of strong AI who believe that a computer that behaves sufficiently intelligently could in fact be intelligent and have consciousness, or mental states, in much the same way that a human does. One example of this is that it is possible using data structures called scripts to produce a system that can be given a story (for example, a story about a man having dinner in a restaurant) and then answer questions (some of which involve a degree of subtlety) about the story. Proponents of strong AI would claim that systems that can extend this ability to deal with arbitrary stories and other problems would be intelligent.

“The limits of my language mean the limits of my world.” -Ludwig Wittgenstein. The Chinese Room Searle’s Chinese Room experiment was based on this idea and is described as follows: An English-speaking human is placed inside a room. This human does not speak any language other than English and in particular has no ability to read, speak, or understand Chinese. Inside the room with the human are a set of cards, upon which are printed Chinese symbols, and a set of instructions that are written in English. A story, in Chinese, is fed into the room through a slot, along with a set of questions about the story.

“The limits of my language mean the limits of my world.” -Ludwig Wittgenstein. The Chinese Room By following the instructions that he has, the human is able to construct answers to the questions from the cards with Chinese symbols and pass them back out through the slot to the questioner. If the system were set up properly, the answers to the questions would be sufficient that the questioner would believe that the room (or the person inside the room) truly understood the story, the questions, and the answers it gave.

“The limits of my language mean the limits of my world.” -Ludwig Wittgenstein. The Chinese Room Searle’s argument is now a simple one. The man in the room does not understand Chinese. The pieces of card do not understand Chinese. The room itself does not understand Chinese, and yet the system as a whole is able to exhibit properties that lead an observer to believe that the system (or some part of it) does understand Chinese In other words, running a computer program that behaves in an intelligent way does not necessarily produce understanding, consciousness, or real intelligence.

“The limits of my language mean the limits of my world.” -Ludwig Wittgenstein. The Chinese Room This argument clearly contrasts with Turing’s view that a computer system that could fool a human into thinking it was human too would actually be intelligent. One response to Searle’s Chinese Room argument, the Systems Reply, claims that although the human in the room does not understand Chinese, the room itself does. In other words, the combination of the room, the human, the cards with Chinese characters, and the instructions form a system that in some sense is capable of understanding Chinese stories. There have been a great number of other objections to Searle’s argument, and the debate continues. [Find more other arguments like the Chinese Room]

Human Brain as a Computer The Halting Problem and Gِodel’s incompleteness theorem tell us that there are some functions that a computer cannot be programmed to compute, and as a result, it would seem to be impossible to program a computer to perform all the computations needed for real consciousness. This is a difficult argument, and one potential response to it is to claim that the human brain is in fact a computer, and that although it must also be limited by the Halting Problem, it is still capable of intelligence.

Human Brain as a Computer Neural Networks is based on the claim that the human brain is a computer. By combining the processing power of individual neurons, we are able to produce artificial neural networks that are capable of solving extremely complex problems, such as recognizing faces. Proponents of strong AI might argue that such successes are steps along the way to producing an electronic human being. Objectors would point out that this is simply a way to solve one small set of problems—not only does it not solve the whole range of problems that humans are capable of, but it also does not in any way exhibit anything approaching consciousness.

HAL—Fantasy or Reality? In the film 2001: A Space Odyssey. One of the main characters in the film is HAL, a Heuristically programmed ALgorithmic computer. In the film, HAL behaves, speaks, and interacts with humans in much the same way that a human would, In fact, this humanity is taken to extremes by the fact that HAL eventually goes mad. In the film, HAL played chess, worked out what people were saying by reading their lips, and engaged in conversation with other humans. How many of these tasks are computers capable of today? [Games, Natural Language Processing, Machine Vision Finally, the likelihood of a computer becoming insane is a rather remote one, although it is of course possible that a malfunction of some kind could cause a computer to exhibit properties not unlike insanity!

Fantasy or Reality? Artificial Intelligence has been widely represented in other films. The Stephen Spielberg film AI:Artificial Intelligence is a good example. In this film, a couple buy a robotic boy to replace their lost son. The audience’s sympathies are for the boy who feels emotions and is clearly as intelligent (if not more so) as a human being. This is strong AI, and while it may be the ultimate goal of some Artificial Intelligence research, even the most optimistic proponents of strong AI would agree that it is not likely to be achieved in the next century

AI in the 21st Century Artificial Intelligence is all around us. Fuzzy logic, for example, is widely used in washing machines, cars, and elevator control mechanisms. (Note that no one would claim that as a result those machines were intelligent, or anything like it! They are simply using techniques that enable them to behave in a more intelligent way than a simpler control mechanism would allow.)

AI in the 21st Century Intelligent agents, are widely used. For example, there are agents that help us to solve problems while using our computers and agents that traverse the Internet, helping us to find documents that might be of interest. The physical embodiment of agents, robots, are also becoming more widely used. Robots are used to explore the oceans and other worlds, being able to travel in environments inhospitable to humans. It is still not the case, as was once predicted, that robots are widely used by households, for example, to carry shopping items or to play with children, although the AIBO robotic dog produced by Sony and other similar toys are a step in this direction.

Part I: Chapter 2: Summary ■ The Chinese Room argument is a thought experiment designed by John Searle, which is designed to refute strong AI. ■ The computer HAL, as described in the film 2001: A Space Odyssey, is not strictly possible using today’s technology, but many of its capabilities are not entirely unrealistic today. ■ The computer program, Deep Blue, beat world chess champion Garry Kasparov in a six-game chess match in This feat has not been repeated, and it does not yet represent the end of human supremacy at this game. ■ Artificial Intelligence is all around us and is widely used in industry, computer games, cars, and other devices, as well as being a valuable tool used in many computer software programs.

Part II: Knowledge Representation “If, for a given problem, we have a means of checking a proposed solution, then we can solve the problem by testing all possible answers. But this always takes much too long to be of practical interest. Any device that can reduce this search may be of value.” -Marvin Minsky, Steps Toward Artificial Intelligence

Part II: Knowledge Representation The way in which the computer represents a problem, the variables it uses, and the operators it applies to those variables can make the difference between an efficient algorithm and an algorithm that doesn’t work at all. This is true of all Artificial Intelligence problems, and as we see in the following, it is vital for search. The example Contact lens problem

Contact lens problem “Imagine that you are looking for a contact lens that you dropped on a football field. You will probably use some knowledge about where you were on the field to help you look for it. If you spent time in only half of the field, you do not need to waste time looking in the other half.”

Contact lens problem Now let us suppose that you are having a computer search the field for the contact lens, and let us further suppose that the computer has access to an omniscient oracle that will answer questions about the field and can accurately identify whether the contact lens is in a particular spot. Now we must choose a representation for the computer to use so that it can formulate the correct questions to ask.

Contact lens problem Representation 1 One representation might be to have the computer divide the field into four equal squares and ask the oracle for each square, “Is the lens in this square?”. This will identify the location on the field of the lens but will not really be very helpful to you because you will still have a large area to search once you find which quarter of the field the lens is in.

Contact lens problem Representation 2 Another representation might be for the computer to have a grid containing a representation of every atom contained in the field. For each atom, the computer could ask its oracle, “Is the lens in contact with this atom?” This would give a very accurate answer indeed, but would be an extremely inefficient way of finding the lens. Even an extremely powerful computer would take a very long time indeed to locate the lens.

Contact lens problem Representation 3 Perhaps a better representation would be to divide the field up into a grid where each square is one foot by one foot and to eliminate all the squares from the grid that you know are nowhere near where you were when you lost the lens. This representation would be much more helpful.

In fact, the representations we have described for the contact lens problem are all really the same representation, but at different levels of granularity. The more difficult problem is to determine the data structure that will be used to represent the problem we are exploring. There are a wide range of representations used in Artificial Intelligence.

When applying Artificial Intelligence to search problems, a useful, efficient, and meaningful representation is essential. In other words, the representation should be such that the computer does not waste too much time on pointless computations, it should be such that the representation really does relate to the problem that is being solved, and it should provide a means by which the computer can actually solve the problem.

Semantic Nets A semantic net is a graph consisting of nodes that are connected by edges. The nodes represent objects. The links between nodes represent relationships between those objects. The links are usually labeled to indicate the nature of the relationship

Semantic Nets Instances

Semantic Nets The links are arrows, meaning that they have a direction. In this way. It may be that Fang does chase Fido as well, but this information is not presented in this diagram. Semantic nets do have limitations, such as the inability to represent negations: “Fido is not a cat.”, this kind of fact can be expressed easily in first-order predicate logic and can also be managed by rule- based systems.