1 Introduction Rachel Ben-Eliyahu

1. Introduction Rachel Ben-Eliyahu - Zohary 1

What is Artificial Intelligence is the Science and Engineering that is concerned with theory and practice of developing systems that exhibit the characteristics we associate with intelligence in human behavior: perception, natural language processing, reasoning, planning and problem solving, learning and adaptation, etc. 2

Descartes (1596 -1650) Dualism vs. Materialism “If there were machines which bore a resemblance to our bodies and imitated our actions as closely as possible for all practical purposes, we should still have two very certain means of recognizing that they were not real men. The first is that they could never use words, or put together signs, as we do in order to declare our thoughts to others… Secondly, even though some machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are acting not from understanding, …” 3

Turing Test § http: //www. robitron. com/Turing. Hub/ § Test proposed by Alan Turing in 1950 § The computer is asked questions by a human interrogator. It passes the test if the interrogator cannot tell whether the responses come from a person § Required capabilities: natural language processing, knowledge representation, automated reasoning, learning, . . . § No physical interaction § Chinese Room (J. Searle, 1980) 4

Chinese Room • A hypothetical system that runs a program that passes the Turing test • But clearly, the program does not understand anything of its inputs and outputs • Conclusion: Running the right program is not a sufficient condition for being a mind 5

Can Machines Act/Think Intelligently? § Yes, if intelligence is narrowly defined as information processing In fact, AI has made impressive achievements showing that tasks initially assumed to require intelligence can be automated § Probably not, if intelligence is not separated from the rest of “human nature” 6

Central goals of Artificial Intelligence Understand the principles that make intelligence possible (in humans, animals, and artificial agents) Developing intelligent machines or agents (no matter whether they operate as humans or not) Formalizing knowledge and mechanizing reasoning in all areas of human endeavor Making the working with computers as easy as working with people 7

History of Artificial Intelligence Stone age (1943 -1956) • Early work on neural networks and logic. • The Logic Theorist (Alan Newell and Herbert Simon) • Birth of AI: Dartmouth workshop - summer 1956 • John Mc. Carthy’s name for the field: Artificial Intelligence 8

History of Artificial Intelligence Early enthusiasm, great expectations (1952 -1969) • Mc. Carthy (1958) • defined Lisp • invented time-sharing • Advice Taker • Learning without knowledge • Neural modeling • Evolutionary learning • Samuel’s checkers player: learning • Robinson’s resolution method. • Minsky: the microworlds (e. g. the block’s world). • Many small demonstrations of “intelligent” behavior. • Simon’s over-optimistic predictions. 9

History of Artificial Intelligence Dark ages (1966 -1973) AI did not scale up: combinatorial explosion The fact that a program can find a solution in principle does not mean that the program contains any of the mechanisms needed to find it in practice. Failure of natural language translation approach based on simple grammars and word dictionary. The famous retranslation English->Russian->English of “the spirit is willing but the flash is weak” into “the vodka is good but the meat is rotten”. Funding for natural language processing stopped. 10

History of Artificial Intelligence Renaissance (1969 -1979) Change of problem solving paradigm: from search-based problem solving to knowledge-based problem solving expert systems: • Dendral: infers molecular structure from the information provided by a mass spectrometer • Mycin: diagnoses blood infections 11

History of Artificial Intelligence Industrial age (1980 -present) • The first successful commercial expert systems. • Many AI companies. • Exploration of different learning strategies (Explanation-based learning, Case-based Reasoning, Genetic algorithms, Neural networks, etc. ) 12

History of Artificial Intelligence The return of neural networks (1986 -present) The reinvention of the back propagation learning algorithm for neural networks first found in 1969 by Bryson and Ho. Many successful applications of neural networks. 13

History of Artificial Intelligence Maturity (1987 -present) Change in the content and methodology of AI research: • build on existing theories rather than propose new ones; • base claims on theorems and experiments rather than on intuition; • show relevance to real-world applications rather than toy examples. 14

History of Artificial Intelligence Intelligent agents (1995 -present) The realization that the previously isolated subfields of AI (speech recognition, planning, robotics, computer vision, machine learning, knowledge representation, etc. ) need to be reorganized when their results are to be tied together into a single agent design. A process of reintegration of different sub-areas of AI to build a “whole agent”: • “agent perspective” of AI • multi-agent systems; • agents for different types of applications, web agents. 15

State of the Art in Artificial Intelligence Deep Blue defeated Kasparov, the chess world champion. PEGASUS, a speech understanding system is able to handle transactions such as finding the cheapest air faire. MARVEL: a real-time expert system monitors the stream of data from the Voyager spacecraft and signals any anomalies. A robotic system drives a car at 55 mph on the public highway. A diagnostic expert system is correcting the diagnosis of a reputable expert. http: //www. youtube. com/watch? v=j. Zm. Nc-rsh. Ww 16

http: //www. youtube. com/watch? v=h. S 0 ZRZ 0 od. TE Four robotic vehicles finished a Pentagonsponsored race across the Mojave desert Saturday (Oct 8, 2005) and achieved a technological milestone by conquering steep dropoffs, obstacles and tunnels over a rugged 132 -mile course without a single human command. 17

What is an intelligent agent An intelligent agent is a system that: • perceives its environment (which may be the physical world, a user via a graphical user interface, a collection of other agents, the Internet, or other complex environment); • reasons to interpret perceptions, draw inferences, solve problems, and determine actions; and • acts upon that environment to realize a set of goals or tasks for which it was designed. input/ sensors user/ environment output/ effectors Intelligent Agent 18

What is an intelligent agent (cont. ) Humans, with multiple, conflicting drives, multiple senses, multiple possible actions, and complex sophisticated control structures, are at the highest end of being an agent. 19

What is an intelligent agent (cont. ) At the low end of being an agent is a thermostat. It continuously senses the room temperature, starting or stopping the heating system each time the current temperature is out of a pre-defined range. 20

What is an intelligent agent (cont. ) The intelligent agents we are concerned with are in between. They are clearly not as capable as humans, but they are significantly more capable than a thermostat. 21

What an intelligent agent can do An intelligent agent can : • collaborate with its user to improve the accomplishment of his or her tasks; • carry out tasks on user’s behalf, and in so doing employs some knowledge of the user's goals or desires; • monitor events or procedures for the user; • advise the user on how to perform a task; • train or teach the user; • help different users collaborate. 22

Characteristic features of intelligent agents Knowledge representation and reasoning Transparency and explanations Ability to communicate Use of huge amounts of knowledge Exploration of huge search spaces Use of heuristics Reasoning with incomplete or conflicting data Ability to learn and adapt 23

Knowledge representation and reasoning An intelligent agent contains an internal representation of its external application domain, where relevant elements of the application domain (objects, relations, classes, laws, actions) are represented as symbolic expressions. This mapping allows the agent to reason about the application domain by performing reasoning processes in the domain model, and transferring the conclusions back into the application domain. ONTOLOGY OBJECT SUBCLASS-OF represents If an object is on top of another object that is itself on top of a third object then the first object is on top of the third object. Application Domain BOOK CUP TABLE INSTANCE-OF CUP 1 ON BOOK 1 ON TABLE 1 RULE x, y, z OBJECT, (ON x y) & (ON y z) (ON x z) Model of the Domain 24

Separation of knowledge from control Implements a general method of interpreting the input problem based on the knowledge from the knowledge base Intelligent Agent Input/ Sensors User/ Environment Output/ Effectors Problem Solving Engine Knowledge Base Ontology Rules/Cases/Methods Data structures that represent the objects from the application domain, general laws governing them, action that can be performed with them, etc. 25

Transparency and explanations The knowledge possessed by the agent and its reasoning processes should be understandable to humans. The agent should have the ability to give explanations of its behavior, what decisions it is making and why. Without transparency it would be very difficult to accept, for instance, a medical diagnosis performed by an intelligent agent. 27

Ability to communicate An agent should be able to communicate with its users or other agents. The communication language should be as natural to the human users as possible. Ideally, it should be free natural language. The problem of natural language understanding and generation is very difficult due to the ambiguity of words and sentences, the paraphrases, ellipses and references which are used in human communication. 28

Illustration: Ambiguity of natural language Words and sentences have multiple meanings Diamond • a mineral consisting of nearly pure carbon in crystalline form, usually colorless, the hardest natural substance known; • a gem or other piece cut from this mineral; • a lozenge-shaped plane figure ( ); • in Baseball, the infield or the whole playing field. Visiting relatives can be boring. • To visit relatives can be boring. • The relatives that visit us can be boring. She told the man that she hated to run alone. • She told the man: I hate to run alone ! • She told the man whom she hated: run alone ! 29

Other difficulties with natural language processing Paraphrase: The same meaning may be expressed by many sentences. Ann gave Bob a cat. Bob was given a cat by Ann. What Ann gave Bob was a cat. Ann gave a cat to Bob. A cat was given to Bob by Ann. Bob received a cat from Ann. Ellipsis: Use of sentences that appear ill-formed because they are incomplete. Typically the parts that are missing have to be extracted from the previous sentences. Bob: What is the length of the ship USS J. F. Kennedy ? Bob: The beam ? John: 1072 John: 130 Reference: Entities may be referred to without giving their names. Bob: What is the length of the ship USS J. F. Kennedy ? Bob: Who is her commander ? John: 1072 John: Captain Nelson. 30

Use of huge amounts of knowledge In order to solve "real-world" problems, an intelligent agent needs a huge amount of domain knowledge in its memory (knowledge base). Example of human-agent dialog: User: The toolbox is locked. Agent: The key is in the drawer. In order to understand such sentences and to respond adequately, the agent needs to have a lot of knowledge about the user, including the goals the user might want to achieve. 31

Use of huge amounts of knowledge (example) User: The toolbox is locked. Agent: Why is he telling me this? I already know that the box is locked. I know he needs to open it. Perhaps he is telling me because he believes I can help. To open it requires a key. He knows it and he knows I know it. The key is in the drawer. If he knew this, he would not tell me that the toolbox is locked. So he must not realize it. To make him know it, I can tell him. I am supposed to help him. The key is in the drawer. 32

Exploration of huge search spaces An intelligent agent usually needs to search huge spaces in order to find solutions to problems. Example 1: A search agent on the internet Example 2: A checkers playing agent 33

Exploration of huge search spaces: illustration Determining the best move with minimax: I Opponent lose I win win lose win win win losedraw win lose win 34

Exploration of huge search spaces: illustration The tree of possibilities is far too large to be fully generated and searched backward from the terminal nodes, for an optimal move. Size of the search space A complete game tree for checkers has been estimated as having 1040 nonterminal nodes. If one assumes that these nodes could be generated at a rate of 3 billion per second, the generation of the whole tree would still require around 1021 centuries ! Checkers is far simpler than chess which, in turn, is generally far simpler than business competitions or military games. 35

Use of heuristics Intelligent agents generally attack problems for which no algorithm is known or feasible, problems that require heuristic methods. A heuristic is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits the search for solutions in large problem spaces. Heuristics do not guarantee optimal solutions. In fact they do not guarantee any solution at all. A useful heuristic is one that offers solutions which are good enough most of the time. 36

Use of heuristics: illustration Heuristic function for board position evaluation: w 1. f 1 + w 2. f 2 + w 3. f 3 + … where wi are real-valued weights and fi are board features (e. g. ……. ) 37

Reasoning with incomplete data The ability to provide some solution even if not all the data relevant to the problem is available at the time a solution is required. Example: The reasoning of a physician intensive care unit. Planning a military course of action. If the EKG test results are not available, but the patient is suffering chest pains, I might still suspect a heart problem. 38

Reasoning with conflicting data The ability to take into account data items that are more or less in contradiction with one another (conflicting data or data corrupted by errors). Example: The reasoning of a military intelligence analyst that has to cope with the deception actions of the enemy. 39

Ability to learn The ability to improve its competence and efficiency. An agent is improving its competence if it learns to solve a broader class of problems, and to make fewer mistakes in problem solving. An agent is improving its efficiency if it learns to solve more efficiently (for instance, by using less time or space resources) the problems from its area of competence. 40

Illustration: concept learning Learn the concept of ill cell by comparing examples of ill cells with examples of healthy cells, and by creating a generalized description of the similarities between the ill cells : Learned concept ((1 ? ) (? dark)) Concept examples ((1 light) (2 dark)) ((1 dark) (2 dark)) + + ((1 light) (2 light)) _ ((1 dark)) + 41

Ability to learn: classification The learned concept is used to diagnose other cells “Ill cell” concept ((1 ? ) (? dark)) Is this cell ill? No Is this cell ill? ((1 light)) ((1 dark) (1 light)) Yes This is an example of reasoning with incomplete information. 42

Extended agent architecture The learning engine implements methods for extending and refining the knowledge in the knowledge base. Intelligent Agent Input/ Sensors User/ Environment Problem Solving Engine Learning Engine Output/ Effectors Knowledge Base Ontology Rules/Cases/Methods 43

Sample tasks for intelligent agents Planning: Finding a set of actions that achieve a certain goal. Example: Determine the actions that need to be performed in order to repair a bridge. Critiquing: Expressing judgments about something according to certain standards. Example: Critiquing a military course of action (or plan) based on the principles of war and the tenets of army operations. Interpretation: Inferring situation description from sensory data. Example: Interpreting gauge readings in a chemical process plant to infer the status of the process. 44

Sample tasks for intelligent agents (cont. ) Prediction: Inferring likely consequences of given situations. Examples: Predicting the damage to crops from some type of insect. Estimating global oil demand from the current geopolitical world situation. Diagnosis: Inferring system malfunctions from observables. Examples: Determining the disease of a patient from the observed symptoms. Locating faults in electrical circuits. Finding defective components in the cooling system of nuclear reactors. Design: Configuring objects under constraints. Example: Designing integrated circuits layouts. 45

Sample tasks for intelligent agents (cont. ) Monitoring: Comparing observations to expected outcomes. Examples: Monitoring instrument readings in a nuclear reactor to detect accident conditions. Assisting patients in an intensive care unit by analyzing data from the monitoring equipment. Debugging: Prescribing remedies for malfunctions. Examples: Suggesting how to tune a computer system to reduce a particular type of performance problem. Choosing a repair procedure to fix a known malfunction in a locomotive. 46

Sample tasks for intelligent agents (cont. ) Instruction: Diagnosing, debugging, and repairing student behavior. Examples: Teaching students a foreign language. Teaching students to troubleshoot electrical circuits. Control: Governing overall system behavior. Example: Managing the manufacturing and distribution of computer systems. Any useful task: Information fusion. Travel planning. Email management. 47

How are agents built A knowledge engineer attempts to understand how a subject matter expert reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base. The expert analyzes the solutions generated by the agent (and often the knowledge base itself) to identify errors, and the knowledge engineer corrects the knowledge base. 48

Why it is hard The knowledge engineer has to become a kind of subject matter expert in order to properly understand expert’s problem solving knowledge. This takes time and effort. Experts express their knowledge informally, using natural language, visual representations and common sense, often omitting essential details that are considered obvious. This form of knowledge is very different from the one in which knowledge has to be represented in the knowledge base (which is formal, precise, and complete). This transfer and transformation of knowledge, from the domain expert through the knowledge engineer to the agent, is long, painful and inefficient (and is known as "the knowledge acquisition bottleneck“ of the AI systems development process). 49

Why are intelligent agents important Humans have limitations that agents may alleviate (e. g. memory for the details that isn’t effected by stress, fatigue or time constraints). Humans and agents could engage in mixed-initiative problem solving that takes advantage of their complementary strengths and reasoning styles. 50

Why are intelligent agents important (cont) The evolution of information technology makes intelligent agents essential components of our future systems and organizations. Our future computers and most of the other systems and tools will gradually become intelligent agents. We have to be able to deal with intelligent agents either as users, or as developers, or as both. 51

Intelligent agents: Conclusion Intelligent agents are systems which can perform tasks requiring knowledge and heuristic methods. Intelligent agents are helpful, enabling us to do our tasks better. Intelligent agents are necessary to cope with the increasing challenges of the information society. 52

Main Areas of AI § Search, especially heuristic search (puzzles, games) § Knowledge representation (including formal logic) § Planning § Reasoning with uncertainty, including probabilistic reasoning § Learning § Agent architectures § Robotics and perception § Natural language processing Agent Robotics Reasoning Search Perception Learning Knowledge Constraint rep. satisfaction Planning Natural language . . . Expert Systems 53

1 Introduction Rachel Ben-Eliyahu — Zohary 1