Logic view markdown
Some notes on logic based on Berkeley's CS 188 course and "Artificial Intelligence" Russel & Norvig 3rd Edition + foundations of rule learning (furnkranz et al. 2014)
logical agents  7.17.7 (omitting 7.5.2)
 knowledgebased agents  intelligence is based on reasoning that operates on internal representations of knowledge
 deductive  generaltospecific
 inductive  specifictogeneral
 3 steps: given a percept, the agent
 adds the percept to its knowledge base (KB)
 asks the knowledge base for the best action
 tells the knowledge base that it has taken that action
 2 approaches
 declarative approach  tell sentences until agent knows how to operate
 know something, can verbalize it
 procedural approach  encodes desired behaviors as program code
 intuitively know how to do it (ex. riding a bike)
 declarative approach  tell sentences until agent knows how to operate
 ex. Wumpus World
 logical entailment between sentences
 $A \vDash B$ means B follows logically from A (A implies B)
 logical inference  showing entailment
 model checking  try everything to see if A $\implies$ B
 this is sound = truthpreserving
 complete  can derive any sentence that is entailed
 TTENTAILS
 recursively enumerate all sentences by assigning true, false to each variable
 check if these are valid within the KB
 if they are, they must also match the query (otherwise return false)
 grounding  connection between logic and real environment (usually sensors)
 theorem properties
 validity  tautalogy  true under all models
 satisfiable  true under some model
 ex. booleanSAT
 monotonicity  set of implications can only increase as info is added to the knowledge base
 if $KB \implies A$ then $KB \land B \implies A$
theorem proving
 resolution rule  resolves different rules with each other  leads to complete inference procedure
 CNF  conjunctive normal form  conjunction (and) of clauses (with ors)
 ex: $ ( \neg A \lor B) \land \neg C \land (D \lor E)$
 anything can be expressed as this
 horn clause  at most one positive
 definite clause  disjunction of literals with exactly one positive: ex. ($A \lor \neg B \lor \neg C$)
 goal clause  no positive: ex. ($\neg A \lor \neg B \lor \neg C$)
 benefits
 easy to understand
 forwardchaining / backwardchaining are applicable
 deciding entailment is linear in size (KB)

forward/backward chaining: checks if q is entailed by KB of definite clauses
 datadriven
 keep adding until query is added or nothing else can be added
 backward chaining works backwards from the query
 goaldriven
 keep going until get back to known facts
 checking satisfiability
 complete backtracking
 davisputnam algorithm = DPLL  like TTentails with 3 improvements
 early termination
 pure symbol heuristic  pure symbol appears with same sign in all clauses, can just set it to the proper value
 unit clause heuristic  clause with just one literal or one literal not already assigned false
 other improvements (similar to search)
 component analysis
 variable and value ordering
 intelligent backtracking
 random restarts
 clever indexing
 davisputnam algorithm = DPLL  like TTentails with 3 improvements
 local search
 evaluation function can just count number of unsatisfied clauses (MINCONFLICTS algorithm for CSPs)
 WALKSAT  randomly chooses between flipping based on MINCONFLICTS and randomly
 runs forever if no soln
 underconstrained problems are easy to find solns
 satisfiability threshold conjecture  for random clauses, probability of satisfiability goes to 0 or 1 based on ratio of clauses to symbols
 hardest problems are at the threshold
 complete backtracking
agents based on propositional logic
 fluents = state variables that change over time
 can index these by time
 effect axioms  specify the effect of an action at the next time step
 frame axioms  assert that all propositions remain the same under actions
 succesorstate axiom: $F^{t+1} \iff ActionCausesF^t \lor (F^t \land ActionCausesNotF^t )$
 ex. $HaveArrow^{t+1} \iff (HaveArrow^t \land \neg Shoot^t)$
 makes things stay the same unles something changes
 stateestimation: keep track of belief state
 can just use 1CNF (conjunctions of literals: ex. $WumpusAlive \land L_2 \land B$)
 1CNF includes all states that are in fact possible given the full percept history
 conservative approximation  contains belief state, but also extraneous stuff
 planning
 could use $A^*$ with entailment
 otherwise, could use SATPLAN
 SATPLAN  how to make plans for future actions that solve the goal by propositional inference
 basic idea
 make assertions
 transitions up to some max time $t_{final}$
 assert that goal is achieved at time $t_{final}$ (ex. havegold)
 present this to a SAT solver
 must add precondition axioms  states that action occurrence requires preconditions to be satisfied
 ex. can’t shoow without arrow
 must add action exclusion axioms  one action at a time
 ex. can’t shoot and move at once
 must add precondition axioms  states that action occurrence requires preconditions to be satisfied
firstorder logic  8.18.3.3
 basically added objects, relations, quantifiers ($\exists, \forall$)
 declarative language  semantics based on a truth relation between sentences and possible worlds
 has compositionality  meaning decomposes
 sapirwhorf hypothesis  understanding of the world is influenced by the language we speak
 3 elements
 objects  john (cannot appear by itself, need boolean value)
 relations  set of tuples (ex. brother(richard, john))
 functions  only one value for given input (ex. leftLeg(john))

sentences return true or false
 combine these things
 firstorder logic assumes more about the world than propositional logic
 epistemological commitments  the possible states of knowledge that a logic allows with respect to each fact
 higherorder logic  views relations and functions as objects in themselves
 firstorder consists of symbols
 constant symbols  stand for objects
 predicate symbols  stand for relations
 function symbols  stand for functions
 arity  fixes number of args
 term  logical expresision tha refers to an object
 atomic sentence  formed from a predicate symbol optionally followed by a parenthesized list of terms
 true if relation holds among objects referred to by the args
 ex. Brother(Richard, John)
 interpretation  specifies exactly which objects, relations and functions are referred to by the symbols
inference in firstorder logic  9.19.4
 propositionalization  can convert firstorder logic to propositional logic and do propositional inference
 universal instantiation  we can infer any sentence obtained by substituting a ground term for the variable
 replace “forall x” with a specific x
 existential instantiation  variable is replaced by a new constant symbol
 replace “there exists x” with a specific x that give a name (called the Skolem constant)
 only need finite subset of propositionalized KB  can stop nested functions at some depth
 semidecidable  algorithms exist that say yes to every entailed sentence, but no algorithm exists that also says no to every nonentailed sentence
 universal instantiation  we can infer any sentence obtained by substituting a ground term for the variable
 generalized modus ponens
 unification  finding substitutions that make different logical expressions look identical
 UNIFY(Knows(John,x), Knows(x,Elizabeth)) = fail .
 use different x’s  standardizing apart
 want most general unifier
 need occur check  S(x) can’t unify with S(S(x))
 UNIFY(Knows(John,x), Knows(x,Elizabeth)) = fail .
 storage and retrieval
 STORE(s)  stores a sentence s into the KB
 FETCH(q)  returns all unifiers such that the query q unifies with some sentence in the KB
 only try to unify reasonable facts using indexing
 query such as Employs(IBM, Richard)
 all possible unifying queries form subsumption lattice
 forward chaining: start w/ atomic sentences + apply modus ponens until no new inferences can be made
 firstorder definite clauses  (remember this is a type of Horn clause)
 Datalog  language restricted to firstorder definite clauses with no function symbols
 simple forwardchaining: FOLFCASK  may not terminate if not entailed
 pattern matching is expensive
 rechecks every rule
 generates irrelevant facts
 efficient forward chaining (solns to above problems)
 conjuct odering problem  find an ordering to solve the conjuncts of the rule premise so the total cost is minimized
 requires heuristics (ex. minimumremainingvalues)
 incremental forward chaining  ignore redundant rules
 every new fact inferred on iteration t must be derived from at least one new fact inferred on iteration t1
 rete algorithm was first to do this
 irrelevant facts can be ignored by backward chaining
 could also use deductive database to keep track of relevant variables
 conjuct odering problem  find an ordering to solve the conjuncts of the rule premise so the total cost is minimized
 backwardchaining
 simple backwardchaining: FOLBCASK
 is a generator  returns multiple times, each giving one possible result
 like DFS  might go forever
 logic programming: algorithm = logic + control
 ex. prolog
 a lot more here
 can have parallelism
 redudant inference / infinite loops because of repeated states and infinite paths
 can use memoization (similar to the dynamic programming that forwardchaining does)
 generally easier than converting it into FOLD
 constraint logic programming  allows variables to be constrained rather than bound
 allows for things with infinite solns
 can use metarules to determine which conjuncts are tried first
 simple backwardchaining: FOLBCASK
classical planning 10.110.2
 planning  devising a plan of action to achieve one’s goals
 Planning Domain Definition Language (PDDL)  uses factored representation of world
 closedworld assumption  fluents that aren’t present are false
 solving the frame problem: only specify result of action in terms of what changes
 requires 4 things (like search w/out path cost function)
 initial state
 actions
 transitions
 goals
 no quantifiers
 set of ground (variablefree) actions can be represented by a single action schema
 like a method with precond and effect
 $Action(Fly(p, from, to))$:
 PRECOND: $At(p, from) \land Plane(p) \land Airport(from) \land Airport(to)$
 EFFECT: $\neg At(p, from) \land At(p, to)$
 can only use variables in the precondition
 problems
 PlanSAT  try to find plan that solves a planning problem
 Bounded PlanSAT  ask whether there is a soln of length k or less
algorithms for planning as statespace search
 forward (progression) statespace search
 very inefficient
 generally forward search is preferred because it’s easier to come up with good heuristics
 backward (regression) relevantstates search
 PDLL makes it easy to regress from a state description to the predecessor state description
 start with a set of things in the goal (and any other fluent can hae any value)
 keep track of a set at all points
 in going backward, the effects that were added need not have been true before, but preconditions must have held before
 heuristics
 ex. ignore preconditions
 ex. ignore delete lists  remove all negative literals
 ex. state abstractions  manytoone mapping from states $\to$ abstract states
 ex. ignore some fluents
 decomposition
 requires subgoal independence
fuzzy logic (lofti zadeh)
 ex blog post
 fuzzy sets  instead of binary membership, can have partial membership $\in [0, 1]$
 intersection of sets  usually take min over memberships
 union of sets  usually take max over memberships
foundations of rule learning (furnkranz et al. 2014)
 terminology  rule has a condition (conjunctive of binary features) + a conclusion = implication
 rule has 2 parts
 antecedent = body  consists of conditions = binary features e.g. $X_1 > 0$, $X_2=0$
 conclusion = consequent* = head
 rule $r$ has length L
 $P, N$  total positives / negatives in the data
 $TP =\hat P, FP =\hat N$  positives / negatives covered (predicted) by a rule
 $FN, TN$  positives / negatives not covered by a rule
 $\frac{TP}{P}$ = true positive rate = sensitivity
 $\frac{TN}{N}$ = true negative rate = specificity
 rules evaluated with a heuristic $H(\hat P, \hat N)$
 rule has 2 parts
categorization of tasks (ch 1)
 historically, a lot of this was developed in the data mining community and gave rise to packages such as WEKA, RAPIDI, KNIME, ORANGE
 historical algos: AQ2 (michalski, 1969), PRISM (cendrowska, 1987), CN2 (Clar & nibett, 1989), FOIL (quinlan, 1990), RIPPER (cohen 1995), PROGOL (muggleton, 1995), ALEPH (srinivasan, 1999)  entire rule workbench in one prolog file, OPUS (webb, 1955), CBA (lui et al. 1998)
 predictive rules
 propositional learning (just propositional logic) v relational learning (firstorder logic) = relational data mining = inductive logic programming
 concept learning  binary classification task
 complete rule set $\mathcal R$  covers all positive examples (recall = 1)
 consistent  rule set $\mathcal R$  covers no negative examples (precision = 1)
 descriptive data mining  usually unsupervised, just learn patterns
 associative rule learning is unsupervised descriptive (e.g. APRIORI)
 here, both the conditions + conclusions can have many features
 subgroup discovery is descriptive, but has a supervised label, so is actually like supervised clustering  goal is to learn subgroups with a significantly different class distribution than the entire population
 associative rule learning is unsupervised descriptive (e.g. APRIORI)
 relational learning  when data doesn’t fit in a table but is associated (e.g. customers have many purchases each)
basics of learning rules (ch 2)
 finding rules is basically a search problem
 want to find best rules (generally bigger coverage, less complexity, higher accuracy)
 can thing of it as searching on a refinement graph  each rule is a node and refinement operators connect rules
 stopping criteria
 threshold for some heuristic
 making final prediction
 final predictions can be made via majority vote, using most accurate rule, or averaging predictions.
 algorithms
 sequential covering (remove covered points after each rule)
(binary split) features (ch 4)
 here, feature means something binary that we split on
 selector is smth of the form $A_i \sim v_{i, j}$ where relation $\sim$ is like $=, >=, <=$
 can also be attribute sets (internal disjunctions) $A_i \in {v_1, v_2, v_3 }$, intervals (range operators), or attribute sets (internal conjunctions)
 can also be simple combinations of binary variables
 relational features  function between features (e.g. length > height)
 can have splits that are functions of previous splits (like a residual DNN connection)
 many algorithms start by making a covering table = table of binary values for all possible (reasonable) splits for all features
 split on all categorical features
 split between all values of continuous features (or ordered discrete)
 can compute relational features (e.g. $A_1  A_2$) by just adding these as features
 feature relevancy
 $pnpair$: pair of training examples where one is positive and one is negative
 totally irrelevant features  don’t distinguish between any positive/negative examples
 a feature $f_1$ is more relevant than another $f_2$ if it separates all the $pn$pairs that $f_2$ does and more
 can also manually set thresholds on TP, FP to decide irrelevance
 missing values
 different types
 missing  was not measured but should have been
 not applicable  e.g. pregnant for a male
 don’t care  could be anything
 basic strategies
 delete incomplement examples
 treat missing as special value
 impute w/ mean/median/linear prediction
 fill in prob distr. over missing val
 pessimistic value strategy  imputed values shouldn’t differentiate between classes  set value so it doesn’t get used (e.g. false for positive class and true for neg class)
 different types
 imprecise values  continuous values with noise
 might want to test variables with $\pm \delta$ handled with pessimistic value strategy
 fuzzy rules  probabilistically split
relational features (ch 5)
 these kinds of task use relational background knowledge + databases
 ex. from knowledge about things like female(X), parent(X, Y), learn that daughter(X, Y):= female(X) , parent(Y, X)
 allow $\forall, \exists$
learning single rules (ch 6)
 search problem to maximize some criteria subject to some constraints
 topdown  start with large cover then go to small
 bottomup  start with highsensitivity, low cover rules then go larger
 search algos
 exhaustive search
 hillclimbing = localsearch  can make less myopic by considering multiple refinements at a time
 beamsearch  keep k best candidates
 bestfirst search  beam search but keep all candidates
 ordered search  prune the search space based on knowledge (e.g. order splitting values)
 levelwise search (e.g. apriori)  generate in parallel all rules with a certain minimum quality
 stochastic search  involves randomness
 search directions: combine topdown (specialization) with bottomup (generalization)
rule evaluation measures (ch 7)
 sometimes we only evaluate the quality of covered rules (e.g. rule list) whereas sometimes we evaluate quality of disjoint sets (e.g. both sides of decision tree split)
 common heuristics are rules that cover a lots of samples or rules that are simple
 equivalent heuristics: compatible heuristics $H$, $G$ rank rules in the same order (antagonistic rank rules in opposite order)
 axes to evaluate rules (want to be close to topleft):
 list of metrics (to maximize), all basically trade of recall / precision
 $Specificity = TN / N$
 $Sensitivity = Recall = TP / P$
 $Support = TP / (P + N)$
 $CovDiff = TP  FP$
 equivalent to classification $Accuracy=(TP + TN) / (P + N)$
 $Coverage = (TP + FN) / (P + N)$  fraction of points covered
 $RateDiff = TP / P  FP / N$
 this is equivalent to more general weighted relative accuracy $LinCost = a \cdot TP  b \cdot FP$
 $Precision = TP / \underbrace{(TP + FP)}_{\text{predicted pos}}$ (sometimes called confidence or rule accuracy)
 RIPPER’s pruning heuristic $(TP  FP) / (TP + FP)$ is equivalent to precision
 covering ratio $TP / FP$ is equivalent to precision
 informationtheoretic measures
 $Info = \log_2 Precision$
 $Entropy =  (Prec \cdot \log_2 Prec + (1Prec) \cdot \log_2 (1Prec))$
 when $TP \leq FN$, same as precision and when $TP > FN$ opposite of precision
 originally developed for case where we aren’t covering positive examples but rather predicting with majority class
 also KLdivergence and Gini index
 $Laplace(r) = (TN + 1)/(TP+1+FP+1)$  pad all the values by 1 to adjust scores when numbers are small
 likelihood ratio statistic  compare distr in rule to distr in full dataset
 complexitybased heuristics
 $Length$

$MDL(r) = I(r) + I(\epsilon r)$  hard to compute