AI - Week 15 Logic and Reasoning in AI Lee McCluskey, room 2/07

Logic and Reasoning in AI Recap A good deal of the data on the will be in the form of ‘Conceptual Knowledge’ has traditionally been represented by diagrams in software engineering / computing. For AI/ Semantic Web we need a precise, symbolic language such as a logic language. Fundamental to logic languages is the idea of INTERPRETATIONS - mapping predicates and constants to some conceptualization of the world. A well formed sentence in Logic is called a Wff. Wff2 LOGICALLY FOLLOWS from Wff1 if and only if every interpretation that makes Wff1 true also makes Wff2 true. Wff2 is LOGICALLY EQUIVALENT to Wff1 if and only if every interpretation that makes Wff1 true also makes Wff2 true AND vice- versa.

Logic and Reasoning in AI Reasoning n Intelligent processes (agents) need to be a good deal more flexible than normal programs - they need to be able to “reason” when carrying out tasks involving negotiation, reasoning, planning etc n As well as being suited to capturing conceptual knowledge, LOGIC is well known for its AUTOMATED REASONING capabilities

Logic and Reasoning in AI Meaning of Quantifiers Consider a Universe with individuals a,b,c,… Ax P(x) = P(a) & P(b) & P(c) & …. Ex P(x) = P(a) V P(b) V P(c) V …. Ax Ay R(x,y) = R(a,a) & R(a,b) & R(a,c) &… & R(b,a) & R(b,b) & R(b,c) & … Ax Ey R(y,x) = Ey R(y,a) & Ey R(y,b) & Ey R(y,c) &… =(R(a,a) V R(b,a) V R(c,a) V …) & (R(a,b) V R(b,b) V R(c,b) V …) & (R(a,c) V R(b,c) V R(c,c) V …) & ….

Logic and Reasoning in AI Meaning of Connectives n The Connectives &, V, ~, ->,,, , <= ETC) Get their meaning via propositional truth tables – PQP V Q ETC TTT TFT FFF FTT

Logic and Reasoning in AI “Laws” These are some well known equivalent FORMS in FOL called laws ( De Morgans laws etc) ¬ ( P & Q ) = ¬P V ¬Q ¬ ( P V Q ) = ¬P & ¬Q P=>Q = ¬P V Q ¬ ¬ P = P P  Q=(P=>Q)&(Q=>P) etc

Logic and Reasoning in AI Quantifiers + Negation LAWS 1. ¬ Ax P(x) = Ex ¬ P(x) 2. ¬ Ex P(x) = Ax ¬ P(x) Similary (and abstractly) ¬ A E = E A ¬

Logic and Reasoning in AI Interpretations revisited Ax Ey R(y,x) Mother_of persons “Given any person there is Someone who is their mother” Greater_than numbers “Given any number there Is some number greater than it” NB Ax Ey … =/= Ey Ax These 2 Interpretations SATISFY this WFF WFF =

Logic and Reasoning in AI Example Revisited “Every student is an academic. Everybody who teaches an academic is an academic. Jeff teaches Fred who is a student.” What can we say about the statement “Jeff is an academic” Translate to FOL: S = student, D = academic, T = teaches Ax S(x)=>D(x) Ax (Ey T(x,y) & D(y)) => D(x) S(Fred) T(Jeff,Fred) Goal: D(Jeff) How can we get agents to automatically deduce such facts??

Logic and Reasoning in AI Another Example Imagine Deep Space 1 travels to Mars and observes many things about the Martians, including the fact that some seem very hostile towards humans. Concrete observations are as follows: (a) All green Martians have antennae. (b) A Martian is friendly to humans if all of its children have antennae. (c) A Martian is green if at least one of its parents is green. On its way back from Mars the robot is hotly pursued by a spacecraft containing green Martians only. Should the robot suspect it is being attacked? Or can the robot reason with its observations to answer the question: `Are all green Martians friendly?'' and hence avert an inter-planetary conflict.

Logic and Reasoning in AI Automated Deduction Roughly, agents can be equipped with the ability to deduce knowledge from observations by automating Laws of Inference A Law of Inference n is a method for deducing wffs n is SOUND if it only ever deduces wffs that also logically follow.

Logic and Reasoning in AI Natural Deduction The most famous Laws of Inference is known by its Latin name “Modus Ponens” From wffs OF THE FORM… P(a) Ax P(x)=>Q(x) We can deduce the following Wff Q(a) Example: Socrates is a Man, All Men are Mortal Deduce: Socrates is Mortal

Logic and Reasoning in AI Natural Deduction Another is called “Modus Tollens” From wffs OF THE FORM… ¬Q(a) Ax P(x)=>Q(x) We can deduce the following Wff ¬P(a) Example: If a thing is smoking then it is on fire. I am not on fire. Deduce: I am not smoking

Logic and Reasoning in AI Unsound Deduction Example: If a person is the murderer then that person must have bloody hands. The Butler has bloody hands. Deduce: The Butler is the murderer This is UNSOUND!!! BH(butler) Ax M(x)=>BH(x) We can’t deduce anything from this!!

Logic and Reasoning in AI Automated Deduction Automating deduction often requires Wffs to be translated to a “clausal form”. Prolog statements are in a type of clausal form. The most common clausal form is when a Wff is expressed as a set of clauses, where each clause in the set is a disjunction of literals, and where any variables are universally quantified. EG {BH(butler), ~M(x) V BH(x) }

Logic and Reasoning in AI Summary FOL is equipped with a form of reasoning called deduction that can be automated Next week I will cover ONE very efficient way to automate deduction.