Philosophical History Aristotle and other Greeks developed the foundations of mathematical logic The mind-body dilemma emerged after the Renaissance –thinking could be studied separately from the physical world –it is necessary to “reconnect” the mind and the body since the interaction is essential for human existence Mental processes are achieved by physical systems, such as the brain or the computer Mental processes like physical processes can be characterized by formal logic or mathematics David Hume said: “Cognition is computation.”

Development of Logic Euler helped develop graph theory which is a foundation of modeling search spaces Charles Babbage and Ada Lovelace (his programmer) worked on the Analytic Engine George Boole developed the mathematics of formal logic Frege helped develop the first order predicate calculus In this century, Russell, Whitehead, and Tarski expanded the development of logic

Turing's Test Questioner: Aims to discover if A or B is the Computer (A) Computer: aims to fool the questioner (B) Human: aims to help the questioner Turing's rationale: "The question and answer method seems to be suitable for introducing almost any one of the fields of human endeavor which we wish to include" (Turing 1950, p.435). What questions would you ask to determine whether A or B is the computer? What would be some of the computer’s strategies to fool the questioner?

The Imitation Game Questioner: Aims to discover if A or B is the Man (A) Male: aims to fool the questioner (B) Female: aims to help the questioner Some of Turing’s Comments "The [imitation] game may perhaps be criticized on the ground that the odds are weighted too heavily against the machine. This objection is a very strong one, but at least we can say that if, nevertheless, a machine can be constructed to play the imitation game satisfactorily, we need not be troubled by this objection" (Turing 1950, p. 435). "in about fifty years' time [by the year 2000] it will be possible to program computers... to make them play the imitation game so well that an average interrogator will have no more than 70 per cent. chance of making the correct identification after five minutes of questioning." (Turing 1950, p.442).

The Turing Test Proposed by Alan Turing in 1950 in Computing Machinery and Intelligence Features –provides an objective method for measuring intelligence –avoids philosophical issues, such as whether a machine is conscious –focuses attention on content and not the means of delivery Weaknesses –only deals with symbolic problem solving –does not include perception or dexterity Some questions –can a computer be original or creative? –Is it impossible to test enough circumstances to judge intelligence?

Other Viewpoints Herbert Simon –intelligent behavior is based on richness of environment and not the complexity of some internal structure –culture is an important factor in forming intelligence Alternatives to the rationalists –language use is based on cultural context and not some abstract meaning –intelligence is not knowing what is true by how to cope in an ever changing world New trends in AI –connectionist networks –artificial life and genetic algorithms –agent-oriented intelligence

Emergent View of Intelligence - 1 Major features –Agents are semi-autonomous –Agents are situated in their local environment and do not know of the “big picture” –Agents are interactional, they cooperate on a particular task –The “society” of agents is structured in a cooperative fashion –Intelligence emerges from the society as a whole, it is more than the sum of parts Can the brain be viewed as a collection of agents? Where do agents appear in silicon in modern devices?

Emergent View of Intelligence - 2 Designing and building such a society –need to represent knowledge –need strategies for searching for alternative solutions –need architectures to support the interaction of agents How could agents be implemented in terms of a programming language?

Game Playing Much of the early work in AI is based on solving puzzles or playing games Emphasizes the state space search process (chapters 3 and 4) Heuristics –useful but potential fallible strategy for solving problems –heuristics help prune the search space so that solutions can be found in a reasonable amount of time Heuristics can be used to control the searching process and can be applied to expert systems too

Strategies for Tic-Tac-Toe How many different arrangements are there for X’s and O’s for a 3x3 tic-tac- toe board, worst case? A win is based on “three in a row” for any row, column, or diagonal Devise a strategy to select the best available move. Try to formulate as an algorithm.

Automated Reasoning Historical roots in theorem proving –Logic Theorist and General Problem Solver (1963) from Newell & Simon –predicate calculus (chapter 2) provides logical foundations –Prolog (chapter 9) provides a programming language implementation Limitations –limited problem domains –inability to distinguish relevant from irrelevant Applications –design of logic circuits, program correctness, complex control systems –often machines and humans work cooperatively to solve problems

A Little Help from my Friends Think of a situation where human problem solving can be aided by a computer. The computer should not do all the work, rather is should work cooperatively with the human or be guided by the human.

Expert Systems Encodes theoretical knowledge and heuristic knowledge based on expert human problem solvers Applications - chemistry (Dendral), infectious disease (Mycin), oil (Prospector), internal medicine (Internist), configuring computer systems (XCON) Limitations –doesn’t capture “deep” knowledge or provide “deep” explanations –lacks robustness and flexibility –difficult to verify, so often not used in life-critical applications –does not learn from experience

Design of an Expert System Suppose you want to design an expert system for simple auto repair to determine why a car won’t start? What questions would you have the computer ask the user?

Natural Language Understanding Efforts in language translation –English-Russian translations motivated by “cold war”, translation between European community motivated by economics in the EEC –Goes well beyond dictionary and syntactic knowledge Requires extensive knowledge about the domain of discourse –often only effective in “blocks world” domains (e.g., SHRDLU) –techniques do not generalize Current approaches –try to find formalizes that are general in structure but can be adapted to a variety of domains –examples, semantic networks and stochastic models

Why is NLP so hard? Give some reasons why you think natural language processing is so difficult?

Modeling Human Performance Many AI applications, even expert systems, are not concerned with human internal mental processes However, in other areas such as human cognition or tutoring systems, modeling internal human performance is critical Cognitive science has adapted many AI methodologies –provides a new vocabulary for developing theories –provides a means to implement, test, and refine theories Intelligent Tutoring Systems (ITS) must model human problem solving if they are to be effective

Teaching Addition How would you design an ITS to teach addition to a fourth grader? You may assume the students already know the 10 x 10 single digit addition table.

Planning and Robotics Planning is finding a sequence of atomic actions to accomplish a particular goal –efficiency is an important consideration –must be able to respond to environmental changes Heuristic problem decomposition is one approach to planning Modern techniques include interactions of multiple semi- autonomous agents Planning will be covered in chapters 5 and 9

Planning Research Why does NASA fund a significant amount of planning research?

Machine Learning - 1 Learning is a critical aspect of intelligent behavior Early applications –Samuel’s checker playing program –Automated Mathematician by Lenat –Meta-DENDRAL learns patterns by examining spectographic data in organic chemistry Modern approaches –using connectionist networks to recognize patterns –using genetic algorithms Although machine learning is a “hot topic” in AI there has only been limited success in particular domains so far

Machine Learning - 2 Name some situations where you think machine learning is appropriate? What characteristics would you need in the programming language used to implement learning?

Emergent Computation Neural architectures –not based on symbols and operations like traditional AI but modeled after human neurons in the brain –knowledge emerges from the entire system based on network connections and threshold values –these systems handle “noise” much better than symbolic systems Advantages –system is distributed so they are more robust –neural systems are more easily parallelized Other emergent models - genetic algorithms and artificial life

Neural Nets Neural nets is a “hot topic” in AI. What have you heard about neural nets and their applications?

AI and Philosophy AI emerges from western rational philosophical traditions but can also contribute to philosophy Physical symbol system hypothesis (Newell and Simon): the necessary and sufficient condition for a physical system to exhibit intelligence is that it be a physical symbol system Natural language is an example of a physical symbol system What do you think of Newell and Simon’s hypothesis? Consider both the necessary and the sufficient.

Environments for developing AI Knowledge structuring –object-oriented systems –expert system frameworks Programming languages –Lisp and Prolog are traditional languages for AI development –these languages allow for rapid prototyping of AI systems –production level tools can be programmed in imperative or object- oriented languages Programming in this class –small programs will be in Lisp and Prolog –the language for the project will be selected based on the project topic