Logical agents

Search limitations

The problem-solving agents based on search algorithms know things, but only in a very limited, inflexible sense (Russel and Norvig 2022, 226):

They know what actions are available (ACTIONS(s)) and what the result of performing a specific action from a specific state will be (RESULT(s,a)). But they don’t know general facts.
Using atomic representations, representing knowledge about the environment (i.e., the state space) requires to list all possible concrete states
The goal can formally only be described as an explicit set of states

Logical thought

Until now, the focus has been on agents that act rationally. Often, however, rational action requires rational (logical) thought on the agent’s part.

Relevant aspects of the environment must be represented in a knowledge base (KB), composed of sentences in knowledge representation language (e.g., logic).

Each sentence represents some assertion about the world (semantics)
The sentences themselves have a causal influence on the agent’s behaviour in a way that is correlated with the contents of the sentences (syntax)
Interaction with the KB through ASK and TELL operations (simplified)
- ASK: asks the KB what action it should perform
- TELL: tells the knowledge base what it perceives

Types of sentences

Axioms: a sentence is taken as being given without being derived from other sentences
Inference: a sentence is derived from old sentence

Propositional logics

Propositional logic (sometimes called zero-order logic) is a very simple logic systems¹.

Objects known as sentences or formulae encode information, which denote some statement about the world.
Atoms are basic sentences and the building blocks of propositional logic. They are usually denoted by “P, Q, R, S, …”, e.g. P might stand for “It is raining”.
Atoms can combine with logical connectives such as “and, or, if then” to form more sentences called compound formulae.
Through reasoning or inference new information can be derived based on existing knowledge about the world.

Knowledge-based agents

Figure 1: A simplified illustration of a knowledge-based agent

A knowledge-based agent uses its knowledge base to

represent its background knowledge,
store its observations (i.e., percepts),
derive actions, and
store its executed actions.

The wumpus world

The example is taken from Russel and Norvig (2022)

Introduction

The wumpus world is an environment in which knowledge-based agents can show their worth.

A cave consisting of rooms connected by passageways
Wumpus is in one room, it eats anyone who enters its room
Some rooms contain bottomless pits
The goal is to survive, to find a heap of gold and to leave the cave

PEAS

Let’s apply the PEAS concept to specify the task environment of the wumpus world (though we do not use the complete specification here).

Performance measure

+1,000 for finding the gold
-1,000 for dying (falling into a pit or being eaten by the wumpus)
-1 for each action taken
The game ends either when the agents dies or finds the gold

Environment

4 x 4 grid with walls surrounding the grid
The agent always starts in \([1,1]\)
The locations of gold and wumpus are chosen randomly (other than \([1,1]\))
Each square other than \([1,1]\) can be a pit, with probability .2
There is a ladder to leave the cave in square \([1,1]\)

Actuators

Move (Go forward, turn right by 90°, turn left by 90°)
Grab (pick up an object in the same square)
Leave the cave (only works in square \([1,1]\))

Sensors

In the square containing the wumpus and in the directly adjacent squares, the agent perceives a stench
In the squares neighboring a pit, the agent perceives a breeze
In the square where the gold is, the agent perceives a glitter
When the agent walks into a wall, it perceives a bump

Percepts are represented as a 4-tuple, e.g.,
\([Stench, Breeze, Glitter, None]\)

The PEAS can be characterized as deterministic, discrete, static, and single-agent

A sample configuration

A typical wumpus world

The agent is in the bottom left corner \([1,1]\), facing east (rightward)
There are three pits in the world (\([4,4]\),\([3,3]\), and \([3,1]\))
Wumpus is in room \([1,3]\)
The gold is in room \([2,3]\)

Exploration

Left: The initial situation after percept \([None,None,None,None]\)
Right: After moving to \([2,1]\) and perceiving \([None,Breeze,None,None]\)

Left: After moving to \([1,1]\) and then \([1,2]\), and perceiving \([Stench,None,None,None]\)
Right: After moving to \([2,2]\) and then \([2,3]\), and perceiving \([Stench,Breeze,Glitter,None]\)

Propositional logic

Basic syntax

Propositions are the building blocks of propositional logic.

Atomic sentences consist of a single proposition symbol, that can be true or false, e.g.,

\(BlockRed\) — The block is red
\(W_{1,3}\) — The wumpus is in \([1,3]\)

The logical connectives and \(\land\), or \(\lor\), not \(\neg\), implies \(\implies\), and if and only if \(\iff\) can be used to build complex sentences.

Operator precedence: \(\neg, \land, \lor, \implies, \iff\) (use brackets when necessary)

Logical connectives

Logical connectives (also called logical operators) are symbols or words used to connect two or more sentences (of either a formal or a natural language) in a grammatically valid way, such that the value of the compound sentence produced depends only on that of the original sentences and on the meaning of the connective.

Conjunction (and)

\(P \land Q\)

Sentence letters are called conjuncts; the two conjuncts can be reversed and retain the original meaning.

Disjunction (or)

\(P \lor Q\)

Sentences letters are called disjuncts; the two disjuncts can be reversed and retain the original meaning. The disjunction is always understood inclusively as an or/and; that is, P might entail, Q might entail, or both P and Q might entail.

Negation (not)

\(\neg P\)

A negated atomic sentence is called a negative literal.

Implication

\(P \implies Q\)

The first sentence letter is called antecedent and second is called consequent. The antecedent and consequent cannot be reversed and still retain its original meaning. Implications are also known as rules of if-then statements.

Biconditional (if and only if, iff)

\(P \iff Q\)

The biconditional combines a conditional relation between P and Q and its reverse. Sentence letters are called “biconditions”; the two biconditions can be reversed and retain the original meaning.

Truth tables

\(\alpha\)	\(\beta\)	\(\neg \alpha\)	\(\alpha \land \beta\)	\(\alpha \lor \beta\)	\(\alpha \implies \beta\)	\(\alpha \iff \beta\)
false	false	true	false	false	true	true
false	true	true	false	true	true	false
true	false	false	false	true	false	false
true	true	false	true	true	true	true

Table 1: Truth tables for the five logical connectives

Basic semantics

Atomic propositions can be true or false.

The truth of a formula follows from the truth of its atomic propositions (truth assignment or interpretation) and the connectives.

Examples:

\((P \lor Q) \land R\)
- If P and Q are false and R is true, the formula is false
- If P and R are true, the formula is true regardless of what Q is
\(B_{2,1} \iff (P_{2,2} \lor P_{3,1})\)
- A square is breezy if a neighboring square has a pit,
  and a square is breezy only if a neighboring square has a pit

A simple KB

Symbols

If we focus on the immutable aspects of the wumpus world, we need the following symbols for each \([x,y]\) location

\(P_{x,y}\) is true if there is a pit in \([x,y]\)
\(W_{x,y}\) is true if there is a wumpus in \([x,y]\), dead or alive
\(B_{x,y}\) is true if there is a breeze in \([x,y]\)
\(S_{x,y}\) is true if there is a stench in \([x,y]\)
\(L_{x,y}\) is true if the agent is in location \([x,y]\)

Sentences

Following sentences² reflect the \(KB\) for the situation depicted in Figure 5 where the agent has moved to \([2,1]\) and received the precepts below:

\([None,Breeze,None,None]\)

\(R_1: \neg P_{1,1}\)
\(R_2: B_{1,1} \iff (P_{1,2} \lor P_{2,1})\)
\(R_3: B_{2,1} \iff (P_{1,1} \lor P_{2,2} \lor P_{3,1})\)
\(R_4: \neg B_{1,1}\)
\(R_5: B_{2,1}\)

A simple inference

Our goal is to decide wether \(KB \models \alpha\)³ for some sentence \(\alpha\),
e.g. is \(\neg P_{1,2}\) entailed by our \(KB\) (and, thus, a safe place to move)?

Model checking enumerates all possible models to check that \(\alpha\) is true in every model in which \(KB\) is true⁴.

Example (the agent has just moved to \([2,1]\)):

Given current proposition symbols there are 2⁷ = 128 possible models⁵
In three of these models \(R_1\) to \(R_5\) (the current KB) are true
In all of these three models, \(P_{1,2}\) is false
So we can say, there is not pit in \([1,2]\)
Only two of the three models, \(P_{2,2}\) is true
So we cannot yet tell whether there is a pit in \([2,2]\)

Propositional proofs

Introduction

Entailment by the \(KB\) (or logical consequence) can also be done by theorem proving.

Theorem proving means applying rules of inference directly to the sentences in the knowledge base to construct a proof of the desired sentence without consulting models.

For large number of models and short length of proofs, theorem proving can be more efficient than model checking.

Additional concepts

Logical equivalence: Two sentences \(\alpha\) and \(\beta\) are logically equivalent if they are true in the same set of models⁶, i.e. \(\alpha \equiv \beta\)⁷

Validity: A sentence is valid if it is true in all models (e.g., \(P \lor \neg P\))⁸

Deduction theorem: For any sentences \(\alpha\) and \(\beta\), \(\alpha \models \beta\) if and only if the sentence \((\alpha \implies \beta)\) is valid.⁹

Satisfiability: A sentence is satisfiable if it is true in, or satisfied by, some model¹⁰

Validity and satisfiability are connected:
\(\alpha \models \beta\) if and only if the sentence \((\alpha \land \neg \beta)\) is unsatisfiable¹¹

Standard logical equivalences

Commutativity of \(\land\) and \(\lor\)	\(\alpha \land \beta \equiv \beta \land \alpha\)	\(\alpha \lor \beta \equiv \beta \lor \alpha\)
Associativity of \(\land\) and \(\lor\)	\((\alpha \land \beta) \land \gamma \equiv \alpha \land (\beta \land \gamma)\)	\((\alpha \lor \beta) \lor \gamma \equiv \alpha \lor (\beta \lor \gamma)\)
Double negation elimination	\(\neg(\neg\alpha) \equiv \alpha\)
Contraposition	\(\alpha \implies \beta \equiv \neg \beta \implies \neg \alpha\)
Implication elimination	\(\alpha \implies \beta \equiv \neg \alpha \lor \beta\)
Biconditional elimination	\(\alpha \iff \beta \equiv (\alpha \implies \beta) \land (\beta \implies \alpha)\)
De Morgan	\(\neg(\alpha \land \beta) \equiv \neg \alpha \lor \neg \beta\)	\(\neg(\alpha \lor \beta) \equiv \neg \alpha \land \neg \beta\)
Distributivity of \(\land\) over \(\lor\) and vice versa	\(\alpha \land (\beta \lor \gamma) \equiv (\alpha \land \beta) \lor (\alpha \land \gamma)\)	\(\alpha \lor (\beta \land \gamma) \equiv (\alpha \lor \beta) \land (\alpha \lor \gamma)\)

Table 2: Logical equivalences (the symbols \(\alpha\), \(\beta\), and \(\gamma\) stand for arbitrary sentences of propositional logic)

Inference rules

Following inference rules¹² can be used in any particular instances where they apply, generating sound inferences without the need for enumerating models:

Modus Ponens¹³: \(\frac{\alpha \implies \beta, \ \alpha}{\beta}\)
The sentence \(\beta\) can be inferred, whenever sentences of the form \(\alpha \implies \beta\) and \(\alpha\) are given
(e.g., “alpha implies beta” is true, and alpha is true, then beta must also be true.)
And-Elimination (Conjunction Elimination): \(\frac{\alpha \land \beta}{\beta}\)
From a conjunction, any of the conjuncts can be inferred
(e.g., If both alpha and beta are true, then beta must be true (and alpha must be true))

All logical equivalences can be used as inference rules

Example (wumpus world)

Rules of the game:

\(R_1: \neg P_{1,1}\)
\(R_2: B_{1,1} \iff (P_{1,2} \lor P_{2,1})\)
\(R_3: B_{2,1} \iff (P_{1,1} \lor P_{2,2} \lor P_{3,1})\)

The agent is in \([1,1]\) and has received following precepts:

\(R_4: \neg B_{1,1}\)

Question: is there pit in\([1,2]\) or \([2,1]\)?

Using logical inference and the \(KB\) containing \(R_1\) to \(R_4\), we prove that \(\neg P_{1,2} \land \neg P_{2,1}\)

1. Biconditional elimination to \(R_2\)

\(R_2: B_{1,1} \iff (P_{1,2} \lor P_{2,1})\)
\(\alpha \equiv B_{1,1}\)
\(\beta \equiv P_{1,2} \lor P_{2,1}\)
Apply \(\alpha \iff \beta \equiv (\alpha \implies \beta) \land (\beta \implies \alpha)\)

Inference: \(R_6 : (B_{1,1} \implies (P_{1,2} \lor P_{2,1})) \land ((P_{1,2} \lor P_{2,1}) \implies B_{1,1})\)

2. And-Elimination to \(R_6\)

\(R_7\) can be inferred from \(R_6\) as follows:

\(R_6 : (B_{1,1} \implies (P_{1,2} \lor P_{2,1})) \land ((P_{1,2} \lor P_{2,1}) \implies B_{1,1})\)
\(\alpha \equiv B_{1,1} \implies (P_{1,2} \lor P_{2,1})\)
\(\beta \equiv (P_{1,2} \lor P_{2,1}) \implies B_{1,1}\)
Apply \(\frac{\alpha \land \beta}{\beta}\) (And-Elimination)

Inference: \(R_7 : (P_{1,2} \lor P_{2,1}) \implies B_{1,1}\)

3. Logical equivalence for contrapositives (\(R_7\))

\(R_8\) can be inferred from \(R_7\) as follows:

\(R_7 : (P_{1,2} \lor P_{2,1}) \implies B_{1,1}\)
\(\alpha \equiv P_{1,2} \lor P_{2,1}\)
\(\beta \equiv B_{1,1}\)
Apply \(\alpha \implies \beta \equiv \neg \beta \implies \neg \alpha\)

Inference: \(R_8 : \neg B_{1,1} \implies \neg (P_{1,2} \lor P_{2,1})\)

4. Modus Ponens with \(R_8\) and the \(R_4\)

\(R_9\) can be inferred from \(R_8\) and \(R_4\) as follows:

\(R_8 : \neg B_{1,1} \implies \neg (P_{1,2} \lor P_{2,1})\)
\(\alpha \equiv \neg B_{1,1}\)
\(\beta \equiv \neg (P_{1,2} \lor P_{2,1})\)
Apply Modus Ponens[^13] \(\frac{\alpha \implies \beta, \ \alpha}{\beta}\)
As \(\neg B_{1,1}\) (\(R_4\)) is given, \(R_9\) can be inferred

Inference: \(R_9 : \neg (P_{1,2} \lor P_{2,1})\)

5. Apply De Morgan’s rule, giving the conclusion

The conclusion (\(R_{10}\)) can be inferred from \(R_9\) as follows:

\(R_9 : \neg (P_{1,2} \lor P_{2,1})\)
\(\alpha \equiv P_{1,2}\)
\(\beta \equiv P_{2,1}\)
Apply \(\neg(\alpha \lor \beta) \equiv (\neg \alpha \land \neg \beta)\)

Inference: \(R_{10} : \neg P_{1,2} \land \neg P_{2,1}\)

Neither \([1,2]\) nor \([2,1]\) contains a pit.

Search algorithms

Search algorithms can be used to find a sequence of steps that constitutes a proof.

The proof problem is defined as follows:

Initial state: The initial knowledge database

Actions: All the inference rules applied to all the sentences in the \(KB\)

Result: Add the inferred sentence to the knowledge base (\(KB\))

Goal: A state that contains the sentence we are trying to prove

Open topics

Completeness (if the available inference rules are inadequate, the goal is not reachable)

Level of complexity (though propositional logic suffices to represent the wumpus world), e.g.,

Inference rules must be set up for each square (expansion of the rule set)
We need a time index for each proposition that refer to an aspect of the world that is subject to change (further expansion of the rule set)

Efficiency of inference algorithms

For further information on these and other matters, please see Russel and Norvig (2022)

Wrap-up

Summary

Rational agents require knowledge of their world to make rational decisions
With the help of a (knowledge) representation language, this knowledge is represented and stored in a knowledge base in the form of sentences
A representation language is defined by its syntax¹⁴ and its semantics¹⁵
Inference is the process of deriving new sentences from old ones
Propositional logic is a simple representation language consisting of propositions symbols and logical connectives
Solutions can be found by enumerating models (model-checking) or inference and proofs using inference rules¹⁶
Propositional logic does not scale to environments of unbounded size¹⁷

xkcd

✏️ Exercises

Logical operators

Explain the logical operators to each other and complete following truth table.

\(\alpha\)	\(\beta\)	\(\neg \alpha\)	\(\alpha \land \beta\)	\(\alpha \lor \beta\)	\(\alpha \implies \beta\)	\(\alpha \iff \beta\)
false	false
false	true
true	false
true	true

Logical equivalences

Check the following equivalences.
If you have issues of understanding, try to find a real life example.

Commutativity of \(\land\) and \(\lor\)	\(\alpha \land \beta \equiv \beta \land \alpha\)	\(\alpha \lor \beta \equiv \beta \lor \alpha\)
Associativity of \(\land\) and \(\lor\)	\((\alpha \land \beta) \land \gamma \equiv \alpha \land (\beta \land \gamma)\)	\((\alpha \lor \beta) \lor \gamma \equiv \alpha \lor (\beta \lor \gamma)\)
Double negation elimination	\(\neg(\neg\alpha) \equiv \alpha\)
Contraposition	\(\alpha \implies \beta \equiv \neg \beta \implies \neg \alpha\)
Implication elimination	\(\alpha \implies \beta \equiv \neg \alpha \lor \beta\)
Biconditional elimination	\(\alpha \iff \beta \equiv (\alpha \implies \beta) \land (\beta \implies \alpha)\)
De Morgan	\(\neg(\alpha \land \beta) \equiv \neg \alpha \lor \neg \beta\)	\(\neg(\alpha \lor \beta) \equiv \neg \alpha \land \neg \beta\)
Distributivity of \(\land\) over \(\lor\) and vice versa	\(\alpha \land (\beta \lor \gamma) \equiv (\alpha \land \beta) \lor (\alpha \land \gamma)\)	\(\alpha \lor (\beta \land \gamma) \equiv (\alpha \lor \beta) \land (\alpha \lor \gamma)\)

Knowledge representation

Explain why knowledge representation languages (KRL) are required.

Research real life AI applications that use (propositional) logic as KRL.

Search of other examples for KRL.

Wumpus world

Suppose the agent has progressed to the point shown in Figure 4 (left part) having perceived nothing in \([1,1]\), a breeze in \([2,1]\), and a stench in \([1,2]\), and is now concerned with the contents of \([1,3]\), \([2,2]\), and \([3,1]\). Each of these can contain a pit, and at most one can contain a wumpus. Construct the set of possible worlds (you should find 32 of them). Mark the worlds in which the KB is true and those in which each of the following sentences is true:

\(\alpha_2\) = “There is no pit in \([2,2]\)”
\(\alpha_3\) = “There is a wumpus in \([1,3]\)”

Hence show that \(KB \models \alpha_2\) and \(KB \models \alpha_3\)

You can use a table like this:

Model	\(KB\)	\(\alpha_2\)	\(\alpha_3\)
\(P_{1,3}\)		true
\(P_{1,3}, P_{3,1}, P_{2,2}\)
…

Propositions not listed as true on a given line are assumed false

Solution note
Proof

Open only if you need help.

There are eight possible combinations of pits in the three squares, and four possibilities for the Wumpus location (including nowhere). We can see that \(KB \models \alpha_2\) because every line where KB is true also has \(\alpha_2\) true. Similarly for \(\alpha_3\).

Model	\(KB\)	\(\alpha_2\)	\(\alpha_3\)
		true
\(P_{1,3}\)		true
\(P_{2,2}\)
\(P_{3,1}\)	true	true
\(P_{1,3},P_{2,2}\)
\(P_{2,2},P_{3,1}\)
\(P_{3,1},P_{1,3}\)		true
\(P_{1,3},P_{3,1},P_{2,2}\)

\(W_{1,3}\)		true	true
\(W_{1,3},P_{1,3}\)		true	true
\(W_{1,3},P_{2,2}\)			true
\(W_{1,3},P_{3,1}\)	true	true	true
\(W_{1,3},P_{1,3},P_{2,2}\)			true
\(W_{1,3},P_{2,2},P_{3,1}\)			true
\(W_{1,3},P_{3,1},P_{1,3}\)		true	true
\(W_{1,3},P_{1,3},P_{3,1},P_{2,2}\)			true

\(W_{3,1}\)		true
\(W_{3,1},P_{1,3}\)		true
\(W_{3,1},P_{2,2}\)
\(W_{3,1},P_{3,1}\)		true
\(W_{3,1},P_{1,3},P_{2,2}\)
\(W_{3,1},P_{2,2},P_{3,1}\)
\(W_{3,1},P_{3,1},P_{1,3}\)		true
\(W_{3,1},P_{1,3},P_{3,1},P_{2,2}\)

\(W_{2,2}\)		true
\(W_{2,2},P_{1,3}\)		true
\(W_{2,2},P_{2,2}\)
\(W_{2,2},P_{3,1}\)		true
\(W_{2,2},P_{1,3},P_{2,2}\)
\(W_{2,2},P_{2,2},P_{3,1}\)
\(W_{2,2},P_{3,1},P_{1,3}\)		true
\(W_{2,2},P_{1,3},P_{3,1},P_{2,2}\)

Table 3: A truth table constructed for the exercise. Propositions not listed as true on a given line are assumed false, and only true entries are shown in the table

Assertions

Prove, or find a counterexample to, each of the following assertions, where \(\alpha\), \(\beta\), and \(\gamma\) represent any logical sentence:

If \(\alpha \models \gamma\) or \(\beta \models \gamma\) (or both) then \((\alpha \land \beta) \models \gamma\)
If \((\alpha \land \beta) \models \gamma\) then \(\alpha \models \gamma\) or \(\beta \models \gamma\) (or both)
If \(\alpha \models (\beta \lor \gamma)\) then \(\alpha \models \beta\) or \(\alpha \models \gamma\) (or both)

Also try to find a real life example

Remember, \(\alpha \models \beta\) means that iff in every model in which \(\alpha\) is true, \(\beta\) is also true.

Open only if you need help.

True. This follows from monotonicity

Andy’s real life example

Consider \(\alpha \equiv A\) and \(\beta \equiv B\)

A = It’s raining
B = Someone pees on the street
\(\gamma\) = The street is wet

It does mean that in any situation

where It’s raining, The street is wet OR
where Someone pees on the street, The street is wet

It also means that in any situation where It’s raining AND Someone pees on the street, The street is wet

False. Consider:
\(\alpha \equiv A, \beta \equiv B, \gamma \equiv (A \land B)\)

	\(\alpha\)	\(\beta\)	\(\alpha \land \beta\)
\(A,B\)	true	true	true
\(A,\neg B\)	true
\(\neg A,B\)		true
\(\neg A,\neg B\)

Table 4: A truth table constructed for I2AI_5 E3b. Remember: for any sentences \(\alpha\) and \(\beta\), \(\alpha \models \beta\) if and only if the sentence \((\alpha \implies \beta)\) is valid. Means that in any model where \(\alpha\) is true, \(\beta\) is true.

Andy’s real life example

A = You are at the HNU
B = It’s Wednesday
\(\gamma\) = Only vegan food is available

(You are at the HNU and It’s Wednesday) implies that Only vegan food is available

But it does not mean that in any situation

where You are at the HNU, Only vegan food is available OR
when It’s Wednesday, Only vegan food is available

There might be occassions

where You are at the HNU and normal food is available OR
when It’s Wednesday and normal food is available

False. Consider
\(\beta \models A, \gamma \models \neg A\)

	\(\alpha = (A \lor \neg A)\)	\(\beta \lor \gamma\)	\(\beta\)	\(\gamma\)
\(A\)	true	true	true
\(\neg A\)	true	true		true

Table 5: A truth table constructed for I2AI_5 E3c. Remember: for any sentences \(\alpha\) and \(\beta\), \(\alpha \models \beta\) if and only if the sentence \((\alpha \implies \beta)\) is valid. Means that in any model where \(\alpha\) is true, \(\beta\) is true.

Propositions

For each English sentence, there is a corresponding logical sentence. Work out which English sentence corresponds to which propositional logic sentence, and hence determine the meaning (in English) of each proposition symbol.

English

There is a Wumpus at (0, 1)
If the agent is at (0, 1) and there is a Wumpus at (0, 1), then the agent is not alive.
The agent is at (0, 0) and there is no Wumpus at (0, 1)
The agent is at (0, 0) or (0, 1), but not both

Propositional Logic

\((C \lor B) \land (\neg C \lor \neg B)\)
\(C \land \neg D\)
\(\neg A \lor \neg(B \land D)\)
\(D\)

Solution note
Sentences

Open only if you need help.

\(A\) = the agent is alive
\(B\) = the agent is at (0, 1)
\(C\) = the agent is at (0, 0)
\(D\) = there is a Wumpus at (0, 1)

Literature

Russel, Stuart, and Peter Norvig. 2022. Artificial Intelligence: A Modern Approach. Harlow: Pearson Education.

Footnotes

Reading recommendation ↩︎
We label each sentence \(R_i\) so that we can refer to them↩︎
\(KB \models \alpha\) means that \(KB\) does entail \(\alpha\) (a sentence \(\alpha\) follows logically from \(KB\))↩︎
In mathematical notation, we write \(M(KB) \subseteq M(\alpha)\)↩︎
7 symbols (\(B_{1,1}\), \(B_{2,1}\), \(P_{1,1}\), \(P_{1,2}\), \(P_{2,1}\), \(P_{2,2}\), \(P_{3,1}\)), two statuses each (true, false); models are assignments of true or false to every proposition symbol↩︎
An alternative definition of equivalence is as follows: any two sentences \(\alpha\) and \(\beta\) are equivalent if and only if each of them entails the other: \(\alpha \equiv \beta\) if and only if \(\alpha \models \beta\) and \(\beta \models \alpha\)↩︎
Note that \(\equiv\) is used to make claims about sentences, while \(\iff\) is used as part of a sentence↩︎
Valid sentences are also known as tautologies, they are necessarily true↩︎
The deduction theorem states that every valid implication sentence describes a legitimate inference↩︎
The knowledge database given earlier (\(R_1 \land R_2 \land R_3 \land R_4 \land R_5\)) is satisfiable because there are three models in which it is true.↩︎
\(\alpha\) is valid iff \(\neg \alpha\) is unsatisfiable; contrapositively, \(\alpha\) is satisfiable iff \(\neg \alpha\) is not valid.↩︎
One or two statements at the upper half of the inference rule imply the statement on the lower half↩︎
Modus ponens is latin and stands for mode that affirms↩︎
Specifies the structure of sentences↩︎
Defines the truth of each sentence in each possible world or model↩︎
Search algorithms can be used to find solutions↩︎
Propositional logic lacks the expressive power to deal concisely with time, space, and universal patterns of relationships among objects.↩︎