TDT4136: Introduction to Artificial Intelligence
"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human."  Alan Turing
Chapter 1  Epic Introduction
Since the dawn of time, humans have tried to define how we think, and this struggle has led us to create artificial intelligence. Historically, four approaches to artificial intelligence have been followed, each described below.
Acting Humanly
If we can't distinguish between a computer and a human, the computer is said to act humanly. The computer's capability to act humanly can be tested by performing a turing test. A computer passes the turing test if a human interrogator cannot tell whether he is communicating with a computer or a person. To pass a turing test, the computer would need to possess the following capabilities:
 Natural language processing to enable it to communicate successfully.
 Knowledge representation to store what it knows or hears.
 Automated reasoning to use the stored information to draw conclusions.
 Machine learning to adapt to new circumstances and to detect patterns.
Thinking humanly
To make a computer think like a human, we must know how humans think. The computers ability to think humanly can be determined by comparing the computer's inputoutput mechanism by the corresponding human behaviour.
Acting Rationally
An agent is something that acts. A rational agent is an agent that does the right thing based on what it knows, its functions, and the surrounding environment; it acts so that it achieves the best expected outcome.
Thinking rationally
Using sound logic rules to reach the right conclusion.
A relevant quote, demonstrating the logical rule of modus ponens: "Socrates is a man; all men are mortal; therefore, Socrates is mortal."
Chapter 2  Intelligent Agents
Agent
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Agent function
Mathematically speaking, we say that an agent’s behaviour is described by the agent function that maps any given percept sequence to an action.
Rational agent
For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever builtin knowledge the agent has.
Task environment
PEAS  Performance, Environment, Actuators, Sensors.
Properties of task environments
Fully Observable vs. Partially Observable
If an agent's sensors has access to the complete state of the environment, at all times, we say that the environment is fully observable. An environment can be partially observable because of noise or inaccurate sensors. It can also be partially observable when sensors only give information about parts of the environment. If an agent doesn't have sensors, we call the environment unobservable.
Single agent vs. multiagent
 Single agent:
 Example: Crossword, sudoku
 Multi agent:
 Example: Chess, ludo
 Competitive multiagent environment:
 E.g. in chess, where two players are playing against each other. One contestant tries to maximize his performance by minimizing the other's.
 Cooperative multiagent environment:
 When driving cars, drivers cooperate not to crash into each other.
Deterministic vs. Stochastic
 Deterministic:
 Any action has a single guaranteed effect. There is no uncertainty about the state that will result from performing an action.
 Strategic:
 If the environment is deterministic except from the actions of other agents, we say that the environment is strategic.
 Stochastic:
 There is some uncertainty about the outcome of an action.
Episodic vs. Sequential
 Episodic:
 The agent's experience is divided into atomic episodes. Each episode consists of the agent perceiving and then performing a single action. The episodes are independent. The choice of action in each episode depends only on the episode itself.
 Sequential:
 The current decision could affect all future decisions.
Static vs. Dynamic
 Static:
 Everything stays unchanged until the agent makes a change.
 Dynamic:
 The environment might change while the agent is considering what to do.
 Semidynamic:
 The environment itself does not change with the passage of time, but the agents performance score does.
Discrete vs. Continous
A discretestate environment such as chess game has a finite number of distinct states. Chess has a discrete set of percepts and actions.
Taxi driving: Continuous state and continuoustime. The speed and location of the taxi and of the other vehicles sweep through a range of continuous values. Taxi driving actions are also continuous (steering angles, etc).
Known vs. Unknown
This isn't so much about the environment as the agent's (or the programmer's) knowledge about the laws of physics in the environment. In a known environment, the outcomes for all actions are given. In an unknown environment you can't predict all outcomes, even if you have a full overview of the environment. This means that the agent must learn from its actions, and how the environment reacts to them.
Various Agent Types
Simple reflex agent
The simplest kind of agent. Selects actions on the basis of the current percept, ignoring the rest of the percept history. The agent will work only if the correct decision can be made on the basis of only the current percept  that is, only if the environment is fully observable (if it's not fully observable, the agent may also make a decision, but it might not be an optimal one). Infinite loops are often unavoidable when operating in partially observable environments. This can sometimes be escaped if the agent randomizes its actions.
Modelbased reflex agents
Maintain internal state to track aspects of the world that are not evident in the current percept. Two kinds of knowledge needs to be represented/coded in the agent program to update the internal state info:
 How the world evolves independently of the agent (e.g., an overtaking car generally will be closer behind than it was a moment ago)
 How the agent's own actions affect the world (e.g., when the agent turns the steering wheel clockwise, the car turns to the right or that after driving for five minutes northbound on the freeway one is usually about five miles north of where one was five minutes ago)
Goalbased agents
Works as modelbased, but also take the agent's goals into account.
Utilitybased agents
Works like the other agents, in addition to trying to maximize their expected “happiness”. Needs a utility function to determine what action leads to the highest performance measure.
Learning agents
Learns from their actions. A learning agent can be divided into four conceptual components:
 Learning element
 Responsible for making improvements.
 Performance element
 Responsible for selecting external actions (In the other agents, this is considered the whole agent).
 Critic
 Gives feedback to the learning element on how the agent is doing and determines how the performance element should be modified to do better in the future. It tells the agent how well it is doing with respect to a fixed performance standard.
 Problem generator
 Responsible for suggesting actions that will lead to new and informative experiences.
Chapter 3  Solving Problems by Searching
Search: is a (preferably methodical) process for finding something.
Uninformed vs. informed:
Do points in the search space give information that helps the searcher to determine the next step?
Uninformed search strategies use only the information available in the problem definition. Some uninformed search strategies are:
 Breadthfirst search
 Depthfirst search
 Uniformcost search
 Depthlimited search
 Iterative deepening depthfirst search
 Bidirectional search
Informed search takes into account the information gained throughout the search, not only the information given in the problem definition. Some informed search strategies:
 Bestfirst search
 AStar search
Partial vs. Complete solutions:
Could the current state of the search always be considered a complete solution? Or is it often a partial state that must be incrementally enhanced to become a solution? Searches with a complete solution is often called local search.
Singlestate problem formulation:
A problem is defined by five components:
 Initial state: The initial state that the agents starts in
 Actions: A description of the possible actions available to the agent
 Transition model: A description of what each action does
 Goal test: Determines whether a given state is a goal state
 Path cost: A function that assigns a numeric cost to each path
A solution is a sequence of actions leading from the initial state to the goal state.
Breadthfirst search
Nodes that are waiting to be explored are placed in a FIFO queue. New successors go at the end.
 Complete: Yes
 Time: O(
$b^d$ )  Horrible  Space: O(
$b^d$ )  Keeps every node in memory  Optimal: Yes, if cost=1 per step. Not optimal in general
Uniformcost search
Like breadthfirst, but the queue is ordered by path cost, lowest first.
Depthfirst search
Unexplored nodes are placed in a LIFO queue. New successors are put at the front.
 Complete: No, fails in infinitedepth spaces, and fails with loops
 Time: O(
$b^d$ )  Horrible  Space: O(V)  Linear in space (Only has to keep track of one ancestors children)
 Optimal: No
Depthlimited search:
Depthfirst search with a depth limit. Does not go further than the depth limit.
Iterative deepening search
You do depthlimited search, but you may be doing several of them. You start with a limit of 1, and increase the limit with +1 for every time you do the search.
 Complete: Yes
 Time: O(
$b^d$ )  Space: O(bd)
 Optimal: Yes, if step cost = 1.
Iterative deepening search uses only linear space, and not much more time than other uninformed algorithms.
Best first search
Idea: use an evaluation function for each node – estimate of “desirability”.
Expand the most desirable unexpanded node. Implementation: nodes are put in a queue sorted in decreasing order of desirability
Special cases: greedy search, A* search
Greedy search
Evaluation function h(n) (heuristic) = estimate of cost from n to the closest goal. Greedy search always expands the node that appears to be closest to the goal.
 Complete: No – can get stuck in loops. But is complete in ﬁnite space with repeatedstate checking.
 Time: O(
$b^d$ )  But a good heuristic can give dramatic improvement.  Space: O(
$b^d$ )  Keeps all nodes in memory.  Optimal: No
A*
A* is a form of bestfirst search where the idea is not to expand "expensive" paths.
A* evaluates nodes with:
The cost of getting to the node.  
Estimated cost of getting from the node to the goal. (heuristic)  
Estimated total cost of passing through n to reach the goal. 
When A* traverses the graph, it follows the path of nodes that gives the lowest value of
A* starts by looking at the root node. It's added to a closed set (of nodes already traversed). All its child nodes are added to the list of open nodes – the open set. Each node maintain a list of parent nodes, and the root node is added as a parent to each of its child nodes. It's also marked as "best parent" for each of them, as it is also the only node that may be their parent.
To continue the search, we pick the node in the open set that has the lowest value for
If the node was in the list from before, we compare their existing
 Complete: Yes, unless there are inﬁnitely many nodes with f ≤ f (G)
 Time: Exponential in [relative error in h × length of solution.]
 Space: Keeps all nodes in memory
 Optimal: Yes—cannot expand fi+1 until fi is ﬁnished
Why A* is optimal
Because the A* algorithm always expands the node with the lowest f(n) value, and the h(n) value never overestimates, it will never be possible to reach the goal with a lower cost than the cost of the node we're expanding. Therefore, the first node we expand, and turns out to be a solution, will also be the optimal solution.
Heuristic:
A heuristic ranks alternatives in a search algorithm based on the available information. Some properties of a good heuristic are:
Admissible  Never overestimates the cost of reaching the goal. This guarantees that we will find the minimum cost path. 
Consistency  The cost of moving is higher than the reduction in the heuristic. The final estimated cost never decreases. 
In gridsearch, a good heuristic may be:
 Manhattan distance  may only move back/forth and sideways.
 Euclidean  The length one would measure with a ruler (straight line distance)
Dominance:
A heuristic (h1) dominates another (h2) if h1(n) ≥ h2(n) for all n.
As long as both heuristics are admissible, we can expect h1 to generate less nodes than h2, and lead to a more efficient search towards the target node. Because of this, it's generally better to choose a heuristic with higher values (provided it's admissible).
Relaxed problems
Admissible heuristics can be derived from the exact solution cost of a relaxed version of the problem
Key point: the optimal solution cost of a relaxed problem is no greater than the optimal solution cost of the real problem
Chapter 4  Beyond Classical Search
Local Search
All nodes in local search are complete solutions. As the search progresses, solutions will become gradually better.
The path is unimportant; only the final state matters. Local search doesn't use heuristics, but a similar concept called objective functions. The objective function answers the question “How optimal are you, solution?”.
Hill climbing
The hill climbing algorithm is Greedy meaning it always moves to states with immediate beneﬁts. It is therefore quick in smooth landscapes, but easily gets stuck in local maxima in rough landscapes.
 Stochastic hill climbing:
 Chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move.
 Firstchoice hill climbing:
 implements stochastic hill climbing by generating successors randomly until one is generated that is better than the current state. This is a good strategy when a state has many (e.g., thousands) of successors.
 Randomrestart hill climbing:
 It conducts a series of hillclimbing searches from randomly generated initial states, until a goal is found. Is complete.
Simulated Annealing
Simulated annealing behaves like hill climbing, but is not greedy in the same sense. Simulated annealing uses a probability for not choosing the neighbor with immediate benefit  which is decreased every iteration. This probability decreases according to how bad the choice is, and as the temperature cools down.
Randomized search for a solution. The algorithm tries to avoid local maxima by allowing some bad solutions.
Simulated annealing starts with a node and an
The current state S is evaluated with the objective function, resulting in the value
If not:
 Generate S's neighbors
 For each neighbor, calculate its objective function
 Pick the neighbor with the highest objective function score.
$P_{max}$  Calculate:
$q = \frac{F(P_{max})  F(S)}{F(S)}$ $p = min(1, e^{q/T})$ , r = random number in range [0,1] If r > p: set
$S = P_{max}$ If not: Randomly pick another neighbor. S = <random neighbor>  T = T  dT
 Check
$F(S) \ge F_{target}$ (S is now a new node/state)
The lower the T, the fewer bad choices are allowed.
Local Beam Search: K parallel searches
A parallel search. Can jump to somebody else's neighbor. K parallel searches are not independent, since the best K neighbors chosen at each step, but some states may contribute 0 or 2+ neighbors to the next step.
Good search = efﬁcient distribution of resources (i.e., the K states).
Jiggle can be introduced via Stochastic Beam Search: pick some of the K neighbors randomly, with probabilities weighted by their evaluations.
Genetic Algorithms
A Genetic Algorithm (GA) is a variant of stochastic beam search in which the successor states are generated by combining two states instead of modifying one; an analogy to natural selection.
Genetic Algorithms begin with a set of k randomly generated states, represented by bit strings, called the population. Each state is rated by the fitness function, which returns higher values for better states. The successor states are then generated by combining two of the states to produce a child state. This is done by choosing a random crossover point x, where the first x bits are the corresponding bits from the first parent, and the rest are from the second parent. The new states are then subject to random mutations.
 Stochastic Local Beam Search with (some of) the K neighbors generated by crossover.
 Supports long jumps in search space, and combinations of the best of both parents.
 Crossover more constructive than destructive...sometimes.
Chapter 5  Adversarial Search
Classes of games:
 Perfect info: state of playing arena and other players holdings known at all times.
 Imperfect info: some info about arena or players not available.
 Deterministic: outcome of any action is certain.
 Stochastic: some actions have probabilities outcomes
MiniMax
Starting at a top node, each possible move is depicted down to a maximum depth, or a terminal node (win or lose position). For the leaf nodes at the bottom level, each node is assigned an evaluation function value. The maximum function assigns the values from the point of view of the players (called Max). The evaluation function must assign high values to win positions and low values to losing positions, and a value in between for nonterminal nodes. A draw is typically assigned a value of 0.
Then, all values are backed up so that a Max node (Max to move) gets the maximum of its daughter nodes (Min nodes), while each Min node gets the minimum value of its daughter nodes (Max nodes). It is only complete if the tree is finite, and it is only optimal against an optimal opponent (meaning the opponent always picks what is best for it self). The tree is explored as a depthfirst search. Heuristic only used at the bottom.
AlphaBeta Pruning
AlphaBeta pruning is a way to make MinMax search much more effective. It is a way of essentially saying “you don’t need to generate any more children, because there is no way we are going to take your path”. It is a method where a node is not evaluated further if it can be proved that the node will never influence the choice. This is done by assigning provisional values (alpha values at Max nodes, beta values at Min nodes) when a value is backed up from below. Only a max node can modify alpha, and only min nodes can modify beta.
The alpha and beta values are defined locally at different nodes. They can also be passed in from above. Pruning does not affect the final result but can change the efficiency of the search
Alpha
A modifiable property of a MAX node. Indicates the lower bound on the evaluating of that MAX node. Updated whenever a larger evaluation is returned from a child MIN node. Used for pruning MIN nodes.
Beta
A modifiable property of a MIN node. Indicates the upper bound on the evaluation of that MIN node. Updated whenever a smaller evaluation is returned from a child MAX node. Used for pruning MAX nodes.
The values (alpha and beta) are used to see if further processing of a branch is necessary. For instance, if a Min node with beta value β is below a Max node with alpha value α, and β <= α then Max will never choose that node anyway, and any further analysis of the subnodes will not change that.
Alphabeta pruning is order dependent.
AI Critics
Critic = a system that evaluates search states. Very similar to both heuristics and objective functions. Many AI applications involve standard AI search algorithms (A*, Minimax, etc.) plus problemspeciﬁc critics. Building the critic is the hard part. Actor = a system for mapping search states to actions. ActorCritic systems use AI to both compute actions and evaluate states. In some versions, the best action is simply the one that leads to a state with the highest evaluation.
Chapter 6  Constraint Satisfaction Problems
A Constraint satisfaction problem (CSP) is a type of statesearch problem defined by:
 a set of variables
 a set of domains (legal values) for these variables
 a set of constraint relations between the variables
A problem is solved when each variable has a value that satisfies all the constraints on the variable. A problem described this way is called a constraint satisfaction problem. Can be represented in a graph where nodes are variables and arcs show constraints, by doing this we can divide the problem into subproblems.
Common for all CSP solutions:
 Initial state: the empty assignment, { }
 Successor function: assign a value to an unassigned variable that does not conﬂict with current assignment. → fail if no legal assignments (not ﬁxable!)
 Goal test: the current assignment is complete
This is common for all CSP algorithms. The intelligence comes from picking the right variables to assign values to, and then picking what value to give them.
Varieties of constraints:
 Unary:
 involve a single variable. Example: Variable A has to be 'green'
 Binary:
 involve pairs of variables. Example: Variable A has to differ from variable B
 Higherorder:
 3 or more variables. Example: AllDiff – All variables need to have different values
 Preferences:
 “better than”constraints  soft constraints. These constraints may be violated in a legal solution.
Consistency:
Node consistency:
A single variable is nodeconsistent if all the values in the variable's domain satisfy the variable's unary constraints. If variable A has the domain {1, 2, 3}, and there's a unary constraint saying variable A can't have evennumbered values, we can make it nodeconsistent by removing 2 from its domain.
We say that a network is nodeconsistent if every variable in the network is nodeconsistent.
Arc consistency:
A variable in CSP is arcconsistent if every value in its domain satisfies the variable's binary constraints. X is arc consistent with Y if for every value in the current domain
We filter on constraints with binary constraints. We filter on one variable at a time. So we use a stack of constraints where we begin with the top constraint and work our way down.
The most popular algorithm for arcconsistency is called the AC3. To make every variable arcconsistent, the AC3 algorithms maintains a queue of arcs to consider. Initially the queue contains all the arcs in the CSP. AC3 then pops off an arbitrary arc {X,Y} from the queue and makes X arcconsistent with Y. If this leaves
Path consistency:
A twovariable set {X,Y} is pathconsistent with respect to a third variable Z, if for every assignment {X=a, Y=b} consistent with the constraints on {X,Y}, there is an assignment to Z that satisfies constraints on {X,Z} and {Y,Z}.
Focus on two or more constraints at a time. So we filter with more than one constraint at a time (X>Z and Y<Z).
Forward checking
Forward checking means that each variable is assigned an apriori set of legal values, and that each assignment leads to a subsequent elimination of all other assignments that become impossible. For instance, whenever a variable X is assigned, for each variable Y that is connected to X by a constraint, delete from Y’s domain any value that is inconsistent with the value chosen for X
Backtracking Search for CSPs
The term backtracking search is used for a depthfirst search that chooses values for one variable at a time and backtracks when a variable has no legal values left to assign. Keeping track on the variables domains to make it easier to detect fail. So when placing a variable on the board all other variables domains must be checked.
The most prominent strategies for solving CSP with backtracking search are:
 Minimum Remaining Value (MRV) Heuristics
 Select the variable with the fewest remaining legal values. Variables with the smallest domain.
 Degree heuristics
 Select the variable which is involved in the largest number of constraints. (It's a good choice when MRV gives a tie)
 Least constraining value
 Prefer the value that rules out the fewest choices of for the remaining variables in the constraint graph.
 Forward checking
 Keep track of remaining legal values for unassigned variables. Terminates when any variable has no legal values.
Local Search for CSPs
When the algorithm starts, the initial state is filled out (e.g. the board is populated with queens) and the search changes one variable at a time (i.e. one queen moves at a time). The point of the algorithm is to eliminate the constraints that are currently violated. Which variable to change is picked at random, but which change is made is determined by the heuristic. The heuristic chooses the move that will violate the fewest constraints (minconflicts), from the current point of view.
Chapter 7  Logical Agents
Knowledge base: set of sentences in a formal language.
Logics: Formal languages to represent information so that conclusions might be made.
Syntax: Defines how sentences are constructed to make sense. You can say "plane a Hannah is in sitting", but it doesn't make sense according to the English syntax rules. If the sentence is changed to "Hannah is sitting in a plane", however, the syntax is valid.
Semantics: Defines the meaning of the sentence.
World: A possible world (model) is a complete set of the truth values of all the logical variables.
Entailment: Means that one thing follows from another.
KB = a if and only if
$M(KB) \subseteq M(a)$
KB “entails” a if KB is a subset of a. That is, if for every world where KB is true, a is also true.
Satisfiable: A sentence is satisfiable if it is true in some model.
Valid: A sentence is valid if it is true for all models.
Number of models: The number of models is the number of models that are true.
Horn Clauses: At most one variable in a logical sentence is unnegated (positive).
Definite Clauses: Exactly one variable in a logical sentence is unnegated (positive).
Goal Clauses: No variable in a logical sentence is unnegated (positive).
Standard logical equivalence
Commutativity of 

Commutativity of 

Associativity of 

Associativity of 

Doublenegation elimination  
Contraposition  
Implication elimination  
Biconditional elimination  
De Morgan  
De Morgan  
Distributivity of 

Distributivity of 
CNF  Conjunction Normal Form
Rewrite logical expressions to a product of sums. In other words, it is an AND of ORs. Example:
Resolution
Cancel out a variable with negated variable. To do resolution first you have to get all your facts onto CNF and then take the negation of what you want to prove and cancel out facts based on what you know. When you can cancel out what you want to prove with what you can derive from what you know then you have solved the problem. It may happen that you derive facts that may be irrelevant to what you are trying to prove.
Chapter 8  FirstOrder Logic
The world in first order logic contains:
 Objects: people, houses, numbers...
 Relations: red, round, prime...
 Functions: father of, best friend...
"Two plus two equals four minus one"
 Objects: one, two, three, four, one plus two, four minus one
 Relation: equals
 Function: plus, minus
In this example, "two plus two" and "four" are two different names for the same object.
The equals relation can be written as
The plus function can be expressed as plus(one, two). Note that this is just a way to denote a function symbol, and note some kind of subroutine that takes two inputs and return an answer.
Quantifiers
 Universal Quantification(∀): For all
 Existential Quantification(∃): There is at least one
Chapter 9  Inference in FirstOrder Logic
Unification: Matching a free variable with a constant. θ = {x/John}
Forward chaining: Start with base facts and try to find out which facts fires which rule. Stop when you have reached the goal.
Backward chaining: Start with the goal and work your way backward. Apply rule to the goal and use unification to bind variables to prove the goal. As we apply a rule we get subproblems and try to solve these. If we can solve the subproblems we can solve the whole problem. (Basically it is a depthfirst search).
Skolemization
Skolemization is the way of removing existential quantifiers by introducing new function symbols.
How: For each existentially quantified variable introduce a nplace function where n is the number of previously appearing universal quantifiers. Special case: introducing constants (trivial functions: no previous universal quantifier).
$\exists x \ P(x) \ \text{becomes} \ P(g)$
$\exists x \ \forall y \ P(x,y) \ \text{becomes} \ \forall y \ P(g,y)$
$\boldsymbol{\forall y} \ \exists x \ P(x,y) \ \text{becomes} \ \forall y \ P(g(\boldsymbol{y}),y) $
$\boldsymbol{\forall y} \ \boldsymbol{\forall z} \ \exists x \ P(x,y,z) \ \text{becomes} \ \forall y \ \forall z \ P(g(\boldsymbol{y},\boldsymbol{z}),y,z) $
$\boldsymbol{\forall y} \ \exists x \ \forall z \ P(x,y,z) \ \text{becomes} \ \forall y \ \forall z \ P(g(\boldsymbol{y}),y,z) $
Conversion to CNF
 Eliminate biconditionals and implications (⇒ and ⇔)
 Move ¬ inwards
 Standardized variables: each quantifier should use a different one
 Each existential variable is replaced by a Skolem function of the enclosing universally quantified variables. (F(x), G(x)...)
 Drop universal quantifiers

Use laws to get it on CNF form

forward chaining in propositional logic (start at facts and apply rules)
 backward chaining in propositional logic (start at goal and make subgoals)
 resolution in propositional logic (negate goal and combine facts with rules)
 forward chaining in firstorder logic (same but with bindings)
 backward chaining in firstorder logic (same but with bindings)
 resolution in firstorder logic (same but with bindings)
Chapter 10  Classical Planning
Plan: A plan is a collection of actions for performing some task.
Stanford Research Institute Planning system (STRIPS)
 Domain
 a set of typed objects; usually represented as propositions
 States
 are represented as firstorder predicates over objects
 Closedworld assumption
 everything not stated is false; the only objects in the world are the ones defined
 Operators/Actions
 defined in terms of preconditions: when can the action be applied? Effects: what happens after the action
Effects are represented in terms of:
 Addlist:
 list of propositions that become true after action
 Deletelist:
 list of propositions that become false after action
 Goals:
 conjunction of literals
PDDL
PDDL (Planning Domain Definition Language) describes the four things we need to define a search problem: the initial state, the actions that are available in a state, the result of applying an action, and the goal test. PDDL accepts negative literals in preconditions and goals.
Two basic approaches
 Statespace planning:
 works at the level of states and actions, Finding a plan is formulated as search through state space. Most similar to constructive search.
 Planspace planning:
 works at the level of plans. Finding a plan is formulated as search through space of plans. Start with partial incorrect plan, then apply changes to correct it. Most similar to iterative improvement
Statespace planning
 Progression planners：
 reason from the start state, trying to ﬁnd the actions that can be applied (match preconditions).
 Regression planners：
 reason from the goal state, trying to ﬁnd the actions that will lead to the start state (match eﬀects).
Partial Order Planning
Search in plan space and use least commitment whenever possible. Least commitment: only make choices that are relevant to solving the current part of the problem
Planspace planning
Planning graph:
Is a directed graph organized into levels: S0 represents the first level which is also the initial state of the graph. The initial state consists of node representing each fluent that holds in S0. The next level A0 consists of nodes representing each ground action that might be applicable to S0. Then alternating levels Si followed by Ai until we reach a termination condition. A persistence action take place if no actions negates it (small empty boxs in the graph). Mutual exclusion links are drawn between states when they are opposite of each other or when the states can not appear together regardless of the choice of action. Meaning that only one of them could be the result. If we get to a point in our graph where two consecutive levels are identical, then the graph has leveled off.
A mutex for actions holds when:
 Inconsistent effects: one action negates an effect of the other.
 Interference: one of the effects of one action is the negation of a precondition of the other.
 Competing needs: one of the preconditions of one action is mutually exclusive with a precondition of the other
A mutex for states holds when:
 Negation: One state is the negation of another.
 Unreachable: All actions leading to the states are mutex.
When a solution exists
For there to exist a plan in the planning graph, the goal states must not be in mutex with each other at the last level. This is not an existence guarantee for a valid solution however, as any states or actions along the path from the start to the goal states must also not be in any mutex relation with each other.
How to find a solution
To extract a solution, start at the level where the goal states are not in mutex with each other. Then search backwards (state search) to find a path where actions are not in mutex with each other. If one is able to arrive at the start state S0, a valid plan exist and that path can be extracted.
Chapter 11  Planning and Acting in the Real World
As of H2019 this i s not part of the syllabus
Critical path method: the path whose total duration is longest.
In Nondeterministic Domains
Extends planning to cover nondeterministic environments (where outcomes of actions are uncertain).
Methods for planning that handle partially observable, nondeterministic and unknown environments include:
Sensorless planning  Environments with no observations 
Contingency planning  Partially observable and nondeterministic environments 
Online planning and replanning  Unknown environments 
In sensorless and partially observable planning, we have to switch from the closedworld assumption to the openworld assumption in which states that are not mentioned have an unknown value (instead of false).
Chapter 12  Knowledge Representation
Knowledgebased systems(KBS):
Is a model of something in the real world (outside the agent). There are 3 main players of modeling:
Knowledge engineer  designs, builds and tests the “expert system” 
Domain expert  possesses the skill and knowledge to find a solution 
End User  the final system should meet the needs of the end user. 
The KB represent the knowledge using a KB language, which is a system for encoding knowledge. The inference engine has the ability to find implicit knowledge by reasoning over the explicit knowledge. Decides what kind of conclusions can be drawn.
Declarative knowledge  Expressed in declarative sentences or indicative propositions. (knowinf of). 
Procedural knowledge  Knowledge exercised in the performance of some task (shopping list). 
Domain knowledge  What we reason about 
Strategic knowledge  How we reason 
5 roles of knowledge representation:
 Surrogate: A representation is a substitute for direct interaction with the world.
 All representations are approximations to reality and they are invariably imperfect
 Fragmentary theory of Intelligent Reasoning
 Is a medium for efficient computation
 Is a medium of human expression
Rule based systems (early KBs expert systems)
 Working memory: contains facts about the world, observed directly or derived from a rule.
 Rule base: contains rules, where each rule is a step in a problem solving process (ifthen format)
 Interpreter: match rules and the current contents of the working memory.
KR languages:
 Syntactic: possible allowed constructions.
 Semantic: What the representation mean, mapping from sentence to world.
 Inferential: Interpreter, what conclusions can be drawn.
Requirements:
 Representation adequacy: Should allow for representing all the required knowledge.
 Inferential adequacy: Should allow inferring new knowledge.
 Inferential efficiency: Inferences should be efficient.
 Clear syntax and semantics.
 Naturalness: Easy to read and use.
Semantic networks:
A semantic net is a structure that shows the relation between concepts, instances and values. The concepts can be categories in sets of entities ("bunches"). The relations can be memberships, subclasses and attributes or others.
Semantic networks store our knowledge in the form of a graph, with nodes representing objects in the world, and arcs representing relationships between those objects. It uses the term inheritance which is used when an object belong to a class and hence inherits all the properties of that class.
Semantic networks can be translated to frames. Nodes then become frame names, links become slot and the node at the other end becomes the slot value. Frames is a collection of attributes (slots) and associated values and constraints on those values. (F stands for false, and T stands for True in the image)
Inheritance in semantic networks
Inheritance of properties are done following the principles:
 If a category has an attribute, then all subcategories have that attribute.
 If a category has a property (e.g. attribute and value), then all subcategories and instances have that property.
The exception is when a semantic entity inherits a property or attribute from several superclasses. Then the following applies:
 In a hierarchy (one parent for each semantic entity), it is the closest property that is valid.
 In a heterarchy (different parents along different paths), categories blocked by interfering inheritance are excluded from inheritance.
Chapter 22  Natural Language Processing
as of H2019 this is not part of the syllabus. It is replaced by "AI and Ethics"
 Knowledge aquisition & language models
Language models
A language model is a model that predicts the probability distribution of language expressions. One of the simplest language models you can make is the probability distribution over a sequence of characters.
Ngram char models
Probability that some sequence of n charachters will appear in a given document.
This can be extended to sentences, and then you have markov chains. Bigram = markov chain of length 2 etc etc.
Def: Corpus is a body
Language identification
You take a given word and compute the probability that it will appear in each language > select maximum
Smoothing ngram models
Words not occuring in a language model will receive a low probability. But a language model could not always guarantee that all the words in the language is there. (Slang, languages evolving etc.) The process of adjusting the probability of lowfrequency counts is called smoothing.
There are different ways of doing this smoothing.
 Laplace smoothing
 If a random Boolean variable
$X$ has been false in all n observations so far then the estimate for$P (X = true )$ should be$\frac{1}{(n + 2)}$ (Performs poorly)  Backof model
 The backoff model, in which we start by estimating ngram counts, but for any particular sequence that has a low (or zero) count, we back off to (n − 1)grams
 Linear interpolation smoothing
 Linear interpolation smoothing is a backoff model that combines trigram, bigram, and unigram models by linear interpolation.
Model evaluation
Use training sets to choose best model for current case. Use perplexity (roughly speaking average branching factor)
Ngram word models
Char model extended for words. Solve out of vocabulary using wildcard words when no match found.
Text classification
This is known as categorization. Use cases => Spam detection. Ex: Categorize text as spam or some other thing based on probabilities..
Use test sets to tune algorithm.
Information retrieval
Characterized by:
 A corpus of documents
 Queries posed in a query language
 A result set
 A presentation of the result set
Models and IR Scoring functions:
 Boolean keyword model. Count matches. Return with most matches.
 BM25 scoring function. For each word multiply the inverse frequency of the word in all documents with the frequency in the current document. Create index for each vocabulary word for faster lookup
Evaluation:
 Precision measures the proportion of documents in the result set that are actually relevant.
 Recall measures the proportion of all the relevant documents in the collection that are in the result set.
$F_1$ score is the summary of both precision (P) and recall (R). That is the harmonic mean:$\frac{2PR}{P + R}$
Refinements:
 Case folding
 Stemming (reduce to similar words)
 Synonyms
 Metadata
The HITS algorithm
Find pages relevant to query. Get all pages in the link neighbourhood. A page is considered an authority if other pages point to it, and a hub if it points to others. Iterate ground up and build list over authority and hubs. Normalize.
Question answering
Requires natural language processing.
Information Extraction.
Return useful information from documents.
We can use
 Finite state automata (Regexes).
 Relational extraction => Implement with cascaded finitestate transducers.
 Process => Tokenize, match on word types (Basic groups) => Combine to complex phrases
So we introduce hidden markov. Tailored for specific queries.
 Can have HMM for e.g. speaker of a sentence etc etc. Uses Bayesian propabilities. Train them on example datasets.
 Improve with linear weights for last n segments of the HMM. Can reward specific words.
Ontology from large corpa: Use regexes with wordclasses instead of variables. Example: NP such as NP (,NP)*(,)? ((and  or)NP)?
Automated template Construction:
 Given a set of initial templates we can derive additional similar facts from some corpus.
 If we combine many of these we can get a machine to learn properly. One template for each word type. Extract data from them in turn.
Summary:
 PLM based on ngrams are pretty decent
 Remember to preprocess due to noise et al.
 Text classification can be done with Bayes ngram.
 Information retrieval uses simple language models based on bags of words, pretty decent.
 Question answering can be handled by information retrieval. Bigger corpus, better results
 Information extraction => Complex models with syntax etc.
MultiagentSystems and Game Theory (not in book)
TODO: Add theory for this part of the curriculum.