Ai Lecture Note

Professor Peter Jackson of the University of Edinburgh classified the history of AI into three periods namelyi) the classical period (of game playing and theorem proving),ii) the romantic period, andiii) The modern period; the major research work carried out during these

periods is presented below.

The Classical PeriodThis period dates back to 1950. The main research works carried out during this period include game playing and theorem proving. The concept of state space approach for solving a problem, which is a useful tool for intelligent problem-solving even now, was originated during this period.The period of classical AI research began with the publication of Shannon’s paper on chess (1950) and ended with the publication by Feigenbaum and Feldman. The major area of research covered under this period is intelligent search problems involved in game-playing and theorem proving.Turing’s “test”, which is a useful tool to test machine intelligence, originated during this period.

The Romantic PeriodThe romantic period started from the mid1960s and continued until the mid1970s. During this period, people were interested in making machines “understand”, by which they usually mean the understanding of natural languages. Winograd’s (1972) SHRDLU system, a program capable of understanding a non-trivial subset of English by representing and reasoning about a restricted domain (a world consisting of toy blocks), in this regard needs special mention. The knowledge representation scheme using special structures like “semantic nets” was originated by Quillian. During this period Minisky (1968) also made a great contribution from the point of view of information processing using semantic nets.

The Modern PeriodThe modern period starts from the latter half of the 1970s to the present day. This period is devoted to solving more complex problems of practical interest. The MYCIN experiments of Stanford University resulted in an expert system that could diagnose and prescribe medicines for infectious bacteriological diseases. The MECHO system for solving problems of Newtonian machines is another expert system that deals with real life problems. It should be added that besides solving real world problems, researchers are also engaged in theoretical research on AI including heuristic search, uncertainty modeling and non-monotonic and spatio-temporal reasoning. To summarize, this period includes research on both theories and practical aspects of AI.

Characteristic Requirements for the Realization of the Intelligent SystemsThe AI problems, irrespective of their type, possess a few common characteristics. Identification of these characteristics is required for designing a common framework for handling AI problems. Some of the well-known characteristic requirements for the realization of the intelligent systems are listed below.

1. Symbolic and Numeric Computation on Common PlatformIt is clear from the previous sections that a general purpose intelligent machine should be able to perform both symbolic and numeric computations on a common platform. Symbolic computing is required in automated reasoning, recognition, matching and inductive as well as analogy-based learning. The need for symbolic computing was felt since the birth of AI in the early fifties. Recently, the connectionist approach for building intelligent machines with structured models like artificial neural nets is receiving more attention. The ANN based models have successfully been applied in learning,

recognition, optimization and also in reasoning problems involved in expert systems. The ANNs have outperformed the classical approach in many applications, including optimization and pattern classification problems. Many AI researchers, thus, are of the opinion that in the long run the connectionist approach will replace the classical approach in all respects. This, however, is a too optimistic proposition, as the current ANNs require significant evolution to cope with the problems involved in logic programming and non-monotonic reasoning. The symbolic and connectionist approach, therefore, will continue co-existing in intelligent machines until the latter, if ever, could replace the former in the coming years.

Non-Deterministic ComputationThe AI problems are usually solved by state-space approach, introduced in section 1.3. This approach calls for designing algorithms for reaching one or more goal states from the selected initial state(s). The transition from one state to the next state is carried out by applying appropriate rules, selected from the given knowledge base. In many circumstances, more than one rule is applicable to a given state for yielding different next states. This informally is referred to as non-determinism. Contrary to the case, when only one rule is applicable to a given state, this system is called deterministic. Generally AI problems are non-deterministic. The issues of determinism and non-determinism are explained here with respect to an illustrative knowledge-based system. For instance, consider a knowledge base consisting of the following production rules and database.

Production RulesPR1: IF (A) AND (B) THEN ( C ).

PR2: IF ( C ) THEN ( D).

PR3: IF ( C ) AND ( E ) THEN (Y).

PR4: IF (Y) THEN (Z).

What is Artificial Intelligence? Systems that think like humans. Machines with minds, in the full and literal sense

Systems that act like humans. The studies of how to make computers do things that, at the moment, people are better. The art of creating machines that performs functions that require intelligence when performed by people.

Systems that think rationally. The studies of mental faculties through the use of computational models. The studies of the computations that make it possible to perceive, reason, and act.

Systems that act rationally. Computational intelligence is the study and design of intelligent agents. Intelligent behavior in artifacts.

From these various definitions, you may think that AI is a relatively new field that is still under change. Although it can be traced back to pre-1800’s the field was not more formally defined until the late 40’s and into the 60’s and 70’s. AI has many intersections with other disciplines, and many approaches to the AI

1. Psych2. Computer Science3. Neuron Science4. Biology5. Math6. Philosophy7. Sociology

We will draw from many different areas that contribute to AI.

The System that think like humans: Closely related to the field of cognitive science, we need to get inside the actual workings of the human mind and implement this in the computer. One approach is by psychological experiment, the other by introspection. Still another is biologically to reconstruct a computer brain in the same manner as human brains.

Systems that act humanelyUnder this approach the goal is to create a system that acts the same way that humans do, but may be implemented in a totally different way. We’ll see the Turing Test shortly which is a way to determine if a system achieves the goal of acting humanely without regard to internal representations. For example, a system might appear to act like a human by inserting random typing errors, but doesn’t actually make errors the same way that a human would.

Systems that think rationallyThere is a tradition of using the “laws of thought” that dates back to Socrates and Aristotle.Their study initiated the field of logic. The logicist tradition within AI hopes to build on this approach to create intelligent systems; the main problem has been scaling this approach up beyond toy systems.

Systems that act rationallyAn agent is something that acts. To distinguish an agent from any other program it is intended to perceive its environment, adapt to change, and operate autonomously. A rational agent is one that acts to achieve the best outcome, or best expected outcome when there is uncertainly. Unlike the “laws of thought” approach, these agents might act on incomplete

knowledge or to still act when it is not possible to prove what it’s the correct thing to do. This approach makes it more general than the “laws of thought” approach and more amenable to scientific development than the pure “human based” approach.

Agent-based activity has focused on the issues of:

1) Autonomy: Agents should be independent and communicate with others as necessary.

2) Situated: Agents should be sensitive to their own surroundings and context.

3) Interactional: Often an interface with not only humans, but also with other agents.

4) Structured: Agents cooperate in a structured society.

5) Emergent: Collection of agents more powerful than an individual agent.

Another way to think about the field of AI is in term of task domains: Expert tasks (you might hire a professional consultant to do), formal tasks (logic, constraints), mundane tasks (common things you do every day).

Mundane:Vision, SpeechNatural Language Processing, Generation, UnderstandingReasoningMotion

Formal:Board Game-Playing, chess, checkers, gobbletLogicCalculusAlgebraVerification, Theorem Proving

Expert:Design, engineering, graphicsArt, creativity:

Music Financial Analysis Consulting

People learn the mundane tasks first. The formal and expert tasks are the most difficult to learn. It made sense to focus early AI work on these task areas, in particular, playing chess, performing medical diagnosis, etc. However, it turns out that these expert tasks actually require much less knowledge than do the mundane skills. Consequently, AI is doing very well in the formal and expert tasks; however it is doing very poorly in the mundane tasks. Example of a mundane task: You are hungry. You have the goal of not being hungry. What do you do to get food? To solve this problem, you have to know what constitutes edible food. You have to know where the food is located. If you do not know where the food is located, you have to find some way to find where it is located, such as looking in phone book or asking someone. You need to navigate to the food. Perhaps the food is in a restaurant. You need to know how to pay for the food, what a restaurant is, what money is, ways to communicate your goals to others, etc… the knowledge necessary to perform this simple task is enormous.

Mundane tasks and the area of broad knowledge understanding are sometimes referred to as “Commonsense Reasoning” and has been termed “AI-Complete” by some researchers.Yet another classification of AI is Weak vs. Strong AI. This is essentially the human vs. non-human

Approach:1) Weak AI: The study and design of machines that perform intelligent tasks. Not concerned with how tasks are performed, mostly concerned with performance and efficiency, such as solutions that are reasonable for NP-Complete problems. E.g., to make a flying machine, use logic and physics, don’t mimic a bird.

2) Strong AI: The study and design of machines that simulate the human mind to perform intelligent tasks. Borrowing many ideas from psychology, neuroscience. Goal is to perform tasks the way a human might do them – which makes sense, since we do have models of human thought and problem solving. Includes psychological ideas in STM, LTM, forgetting, language and genetics.

3) Evolutionary AI: The study and design of machines that simulate simple creatures, and attempt to evolve and have higher level emergent behavior. For example, ants, bees, etc.Philosophical Foundations Underlying assumption of Strong AI is the physical symbol hypothesis, defined by Newell and Simon in 1976.Physical symbol hypothesis states: The thinking mind consists of the manipulation of symbols. That is, a physical symbol system has the necessary and sufficient means for general intelligent action. If this hypothesis is true, then it means that a computer (which merely manipulates symbols) can perform generally intelligent actions. This claim has been rebuked by many researchers citing arguments of consciousness, self-

awareness, or quantum theory. David Chalmers has proposed some interesting thought experiments if brain cells were replaced by transistors, and consciousness is graphed vs. transistors.

Turing Test: Proposed by Alan Turing in 1950 as a way to define intelligence. His test is that if the computer should be interrogated by a human through a modem or remote link, and passes the test if the interrogator cannot tell if there is a human or computer at the other end. No computer today can pass the test in a general domain, although computers have used “tricks” to pass in limited domains (e.g. Eliza, Julia). But is something intelligent if it is perceived to be intelligent? Many complaints about the Turing Test; note that humans often mistake humans on the other end as computers! A famous argument is Searle’s Chinese Room. Consider a room, closed off from the world except for an envelope drop. Inside the room is a human with a rule book written in English and stacks of paper for writing. The rule book tells the human how to transcribe from Chinese to English.Naturally, the set of rules is terribly complex, but one can imagine it’s possible. Now, if someone drops a letter written in Chinese through the slot, the human can follow the rules in the book (perhaps writing intermediate steps) and produce some English output. Question: Does the human understand Chinese?Searle says no, he is just following rules; consequently computers will never “understand” a language like Chinese the same was as humans. (Searle does claim consciousness is an emergent process of neural activity).

Other objections to the Turing test point out that it is biased purely toward symbolic problem-solving skills. Perceptual skill or manual dexterity is left out. Similarly, the test is biased towards humans – it may be possible to have intelligence that is entirely different from human intelligence. After all, why should a computer be as slow as humans to add numbers? Perhaps one of the largest objections is that of the situational intelligence required. To really

pass the Turing Test, some have argued that a machine must be raised and brought up in the same culture and society of humans. How else would a machine know that it is not appropriate to call a “throne” a “chair”? (One answer is to painstakingly enter information like this by hand).In 1990 Hugh Loebner agreed with The Cambridge Center for Behavioral Studies to underwrite a contest designed to implement the Turing Test. Dr. Loebner pledged a Grand Prize of $100,000 and a Gold Medal for the first computer whose responses were indistinguishable from a human's. Each year an annual prize of $2000 and a bronze medal is awarded to the most human computer. The winner of the annual contest is the best entry relative to other entries that year, irrespective of how good it is in an absolute sense. A short snippet of interaction from the winning program in 2009 (the program is “Do-Much-More”):

AI Applications

Although AI has sometimes been loudly criticized by industry, the media, and academia, there have been many success stories. The criticism has come mainly as a result of hype. For many years, AI was hailed as solving problems such as natural language processing and commonsense reasoning, and it turned out that these problems were more difficult than expected. Here are just a few applications of artificial intelligence.

1. Game-playing. IBM’s deep-blue has beaten Kasparov, and we have a world-champion caliber Backgammon program. The success here is due to heuristic search and the brute-force power of computers. AI path-finding algorithms and strategy have also been applied to many commercial games, such as WoW or Command & Conquer. The emphasis on game AI is on developing rational agents to match or super cede human performance. AI has continues to improve such that you cannot distinguish between computer and human players.

Computer games also must obey some natural rules just like human: Obey the rules of the game Conscious of the environment by characters Finding it path Decision making Planning

Game AI can intimate human in the following ways: Smart Emotional feelings Body language to communicate human feelings Being integrated to the environment Rational behavior but unpredictable

2. Automated reasoning and theorem-proving. Newell and Simon are pioneers in this area, when they created the Logic Theorist program in 1963. Logic Theorist proved logical assertions and this helped define propositional calculus and eventually programming languages like Prolog. Formal mathematical logic has been important in fields like chip verification and mission-critical applications such as space missions. Reasoning can be seen as logical thinking based on past experience and knowledge of an event. This is refers to as human intelligence that is invoke into computer to reason logically. Automated reasoning is the science that enable computer to apply logical reasons so complex tasks which include theorem proving, circuit designs and puzzle solving.

3. Expert Systems: An expert system is a computer program with deep knowledge in a specific niche area that provides assistance to a user. Famous examples include DENDREAL, an expert system that inferred the structure of organic molecules from their spectrographic information, and MYCIN, an expert system that diagnosed blood

diseases with better accuracy than human experts. More common examples of expert systems include programs like “Turbo Tax” or Microsoft’s help system. Typically, a human has to program the expert knowledge into these systems, and they operate only within one domain with little or no learning.

The goal of expert systems is to make expertise who are available decision making who need answers quickly. By definition expert system simulates human expert thought process to solve complex task in a specific domain.KNOWLEDGE ENGINEERING: the process of building an expert system is called knowledge engineering. Knowledge engineers acquire the knowledge from a human expert or other source and code in the expert system. The problem of transferring human knowledge into an expert system is so major that it is called the knowledge acquisition bottleneck, major bottlenecks are due to: cognitive barrier, linguistic barrier, representing barrier and creating model Conventional programs vs. ESESs differs from the conventional computer programs in the following aspects:

ESs are knowledge intensive programs ESs are highly interactive ESs mimic human experts in decision making and reasoning process ESs divides expert knowledge into number of separate rules ESs are user friendly and intelligent

ES ARCHITECTURE: an ES is a specific problem domain. It is for problem solving and not for modeling. The expert system consists of;

1) A knowledge base2) A working memory3) An inference engine4) User interface5) System analysis, graphic and other software.

Knowledge base consists of declarative knowledge that are facts about the domain and procedural knowledge that are heuristic rules the domain. The working memory is the active set of knowledge base. Inference engine is the problem solving module. It also gives justification (explanation) for the advice from the ES. Communication modules help in interaction between other modules and also provide user-developer interfaces.KNOWLEDGE BASE:Knowledge base module contains domain specific. Knowledge can either be;

1) Prior knowledge: which comes before and it independent of knowledge from the senses. It is consider being universally true and cannot be denied by contradiction e.g. every night turns day.

2) Posterior knowledge: that is derived from the senses. It can be denied on the basis of new knowledge without the necessity of contradiction. The light is green.

Knowledge can be represented in various forms: Rules Semantic nets Frames Scripts

Objected oriented Conceptual graph and so onRULES: the most popular format of rules are the IF –condition –THEN – action statements.IF it raining AND you are going outTHEN take umbrella SEMANTIC NETS: this representation is used when knowledge is a subset of some other bigger set. A semantic network consists of nodes connected by links that describe the relation between nodes.Frames: schema is used to describe a more complex knowledge structure (than semantic nets) and the frame is one type of schema. Frame is data structure for representing stereotyped situation (Minsky, 1975). Frames represent objects as sets of slot/filler pairs.ES DEVELOPEMNT

I. IDENTIFICATION: determining characteristics of the problem.II. CONCEPTUALIZATION: finding concept to represent knowledge.III. FORMALIZATION: designing the structure to organize the knowledgeIV. IMPLEMENTATION: formulating the rules embodying knowledgeV. TESTING: validating the rules

Methods of simulating the performance of the expert are list below: The creation of a knowledge base which use some knowledge

representation structure to capture the knowledge of the area matter. Gathering of knowledge from the subject area matter and codifying it

according to the structure which is called knowledge engineering Once the system is developed it is placed in the same real world

problem solving situation as the human SAM, typically as an aid to human workers or as a supplement to some information system. Expert system may have or may not have learning components.

Building an expert system is done by knowledge engineers and it’s called knowledge engineering. It is the duty of the knowledge engineers to ensure that system has all the knowledge needed to solve a problem in an area. The knowledge engineer must also choose one or more forms in which to represent the required knowledge as symbol patterns in the memory of the system.Building blocks of expert systems consist of two fundamental parts:

The knowledge repository (has both factual and heuristic knowledge). An inference engine (reasoning faculty).

4. Machine Learning. Systems that can automatically classify data and learn from new examples has become more popular, especially as the Internet has grown and spawned applications that require personalized agents to learn a user’s interests. Some examples include cars capable of driving themselves, face and speech recognition, and Internet portals with pre-classified hierarchies.

5. Natural Language Understanding, Semantic Modeling. This area has been successful in limited domains. Most attention has shifted to a shallow understanding of natural language, i.e. witness the various search-engine technologies on the WWW, some that understand rudimentary questions.

6. Modeling Human Performance. As described earlier, machine intelligence need not pattern itself after human intelligence. Indeed, many AI programs are engineered to solve useful problems without regard for their similarities to human mental architecture. These systems give us another benchmark to understand and model human performance. Many cognitive scientists use computer techniques to construct their psychological models.

7. Planning and Robotics. Planning research began as an effort to design robots that could perform their task. For example, the Sojourner robot on Mars was able to perform some of its own navigation tasks since the time delay to earth makes real-time control impossible. Planning is the task of putting together some sequence of atomic actions to achieve a goal. This area of work extends beyond robots today; for example, consider a web “bot” that puts together a complete travel or vacation package for a customer. It must find reasonable connections and activities in each stop.

8. Languages and Environments. LISP and PROLOG were designed to help support AI, along with constructs such as object-oriented design and knowledge bases. Some of these ideas are now common in mainstream programming languages.

9. Alternative Representations, e.g. Neural Networks and Genetic Algorithms. These are bottom-up approaches to intelligence, based on modeling individual neurons in a brain or the evolutionary process.

10. AI and Philosophy. We have briefly touched on some of the philosophical issues, but there are many more. What happens if we do have intelligent computers? Should they have the same rights as people? What are the ethical issues? What is knowledge? Can knowledge be represented? TheInference engine: this module examines the knowledge base and answers the questions (how and why) from the user. It is the most crucial component of ES. It derives the knowledge i.e. guides the selection of a proper response to a specific situation called pruning. There are three formal approaches used in this case are

Production rules Structured objects Predicate logic

PRODUCTION RULES: consists of a set, a rule interpreter which specifies when and how to apply the rules and a working memory which holds the data, goals and intermediate results. STRUCTURE OBJECTS: use vector representation of essential and accidental properties.PREDICATE: uses prepositional and predicate calculi.The inference engine can work in the following ways:

1. Forward chaining2. Backward chaining3. Abduction4. Reasoning under uncertainty1) Forward chaining (bottom-up reasoning):

It starts the known initial state and proceeds in the forward direction to achieve the goal. The inference engine searches the knowledge base with the given information for rules whose precedence matches the given current state. The basic steps are;

i. The system is given one or more conditionsii. The system searches the rules in the knowledge base for each

condition. Those rules that correspond to the condition in IF part are selected.

iii. Each rule can generate new conditions from the conclusions of the invoked THEN part, which in turn are again added to the existing ones

iv. The added conditions, if any will be processed again (step II). THE session ends if there are no new conditions.

2) BACKWARD CHAINING (TOP-DOWN REASONING):Reasoning is done in the backward direction. The system selects a goal state and reasons in the backward direction. The initial condition is established for the goal to be true. If the given initial state conditions matches with the established ones, then the goal is the solution. Otherwise, the system selects another goal and the process is repeated. The basic steps are: (1). Select a goal state and rules whose THEN portion has the goal state as conclusion(2). Establish sub goals to be satisfied for the goal state to be true. From the IF portion of the selected rules.(3). Establish initial conditions necessary to satisfy all sub goals.(4). Check whether the given initial state matches with the established ones. If so, the goal is one solution. If so, select another state.

3) ABDUCTION:Reasoning from observed facts to the best explanation.P q, q proves pAbduction is related to backward chaining and implication. Abduction is a mathematically justifiable, practical and reasonably way to generate hypotheses. Abduction is another name for a fallacious argument. It is not guaranteed to work.

Summary of the purpose of forward chaining, backward chaining and abduction.inference start PurposeForward chaining Facts Conclusions that must followBackward chaining Uncertain

conclusionFacts to support the conclusions

Abduction True conclusion Facts which may follow

PROBLEM DOMAIN:Is a special problem area such as medicine, finance, science or engineering. Where an expert can conveniently solve a problem very well.

An expert knowledge is specific to one problem domain as opposed to general knowledge.Expertize in one problem domain cannot automatically carry over another problem domain.Knowledge domain: The expert knowledge about solving a specific problem. E.g. medical expert system, have knowledge about certain symptoms caused by infectious disease and treatment.ADVANTAGES OF EXPERT SYSTEM

Increased availability Reduced cost Multiple expertize Explanation Intelligent tutor Steady, un-emotion and complete response at all times Reduced danger Performance Increased reliability Fast response Intelligent database

Rule representation The knowledge of an expert system can be represented in number of ways;Rules and object e.g. IF the light is red THEN stopIF the light is green THEN goIF the light is orange THEN get readyEXPERT SYSTEM DEVELOPMENT:Limitations of expert systems:

1. Lack of casual knowledge : it does not have an understanding off the causes and effects in a system

2. Expertize is limited to the knowledge domain that the systems know about

3. It cannot reason about new situation the way people can4. It is a time consuming and labor intensive task.

RULE BASED SYSTEMS: Rule base system represents knowledge in terms of a bunch of rules that tells you what you should do or what you could conclude in different situations. If rule base system consists of a bunch of IF THEN rules, a bunch of facts and some interpreter controlling the application of the rules.There are two kinds of rule based systemThere are two kinds of rule based system

a. Forward chaining system b. Backward charity system

Forward chaining system starts with initial facts and keep using the rules to draw new conclusions given those facts for example.

Rule 1: forward chaining is primarily data- driven

If the car is overheats, then the car will stall

Rule2: IF the stalls

THEN it will cost money

AND I will be late getting home.

Backward chaining you start with hypothesis that you are typing to prove and keep looking for rules that would allow you to conclude the hypothesis. It is goal driven

Example

Rule 1:

IF the car is not tuned and the battery is weak

THEN not enough current will reach the starter

IF not enough current reaches the starter

IF not enough current reaches the starter

THEN the car will not start.

Areas of AI

Vision Robotics Natural language Understanding Speech Expert system Artificial neural systems

The process of building an expert system

1. The knowledge engineer establishes a dialog with the human expert to elicit knowledge.

2. The knowledge engineer codes the knowledge explicitly in the knowledge base

3. The expert evaluates the expert system and give critique to the knowledge engineerEarly expert system

DENDRAL:

Is a chemical mass spectroscopy to identity chemical constituents such as carbon, hydrogen, nitrogen e.g. it is used in industry and in academia.

MYCIN: is a computer program designed to assist physicians in the diagnosis of infectious diseases

DIPMETER: helps in the analysis of data gathering during oil exploration.

PROSPECTOR: is used in geological data analysis for minerals

XCON/R1: used in configuring computer system

Elements of an expert system

Knowledge base= rules Working memory: facts used by rules Interface engine: makes inferences deciding which rules are satisfied

and prioritizing Agenda: a prioritized list of rules in the inference engine Explanation facility: explains the reasoning of an expert system Knowledge acquisition facility: the user to enter knowledge in the

system by passing the knowledge engineer

User interface: mechanism by which user and system communicate

Procedural paradigms: the programmer must specify exactly how a problem solution must be coded (algorithm).

Non procedural paradigms: the programmer does not give exact details on how the program is to be solved

Imperative language: uses a sequence of statement to determine how to reach a certain goal for example java program.

A functional language: use a different paradigm than imperative language. They use side effect free functions as a basic building block in the language. This enables lots of things and makes lots of things more difficult for in most cases different from what people are used to e.g. LISP, PROLOG

BUILDING RULE –BASED SYSTEMS WITH IDENTIFICATION OF TREES

SEMANTIC NETWORK: is the basic structure