Epistemic modal logic is a subfield of modal logic that is concerned with reasoning about knowledge. While epistemology has a long philosophical tradition dating back to Ancient Greece, epistemic logic is a much more recent development with applications in many fields, including philosophy, theoretical computer science, artificial intelligence, economics and linguistics. While philosophers since Aristotle have discussed modal logic, and Medieval philosophers such as Ockham and Duns Scotus developed many of their observations, it was C. I. Lewis who created the first symbolic and systematic approach to the topic, in 1912. It continued to mature as a field, reaching its modern form in 1963 with the work of Kripke.
Historical development
Many papers were written in the fifties that spoke of a logic of knowledge in passing, but it was Finnish philosopher von Wright's paper An Essay in Modal Logic from 1951 that is seen as a founding document. It was not until 1962 that another Finn, Hintikka, would write Knowledge and Belief, the first booklength work to suggest using modalities to capture the semantics of knowledge rather than the alethic statements typically discussed in modal logic. This work laid much of the groundwork for the subject, but a great deal of research has taken place since that time. For example, epistemic logic has been combined recently with some ideas from dynamic logic to create public announcement logic and product update logic, which attempt to model the epistemic subtleties of conversations. The seminal works in this field are by Plaza, van Benthem, and Baltag, Moss, and Solecki.
Standard possible worlds model
Most attempts at modeling knowledge have been based on the possible worlds model. In order to do this, we must divide the set of possible worlds between those that are compatible with an agent's knowledge, and those that are not. This generally conforms with common usage. If I know that it is either Friday or Saturday, then I know for sure that it is not Thursday. There is no possible world compatible with my knowledge where it is Thursday, since in all these worlds it is either Friday or Saturday. While we will primarily be discussing the logicbased approach to accomplishing this task, it is worthwhile to mention here the other primary method in use, the eventbased approach. In this particular usage, events are sets of possible worlds, and knowledge is an operator on events. Though the strategies are closely related, there are two important distinctions to be made between them:

The underlying mathematical model of the logicbased approach are Kripke semantics, while the eventbased approach employs the related Aumann structures.

In the eventbased approach logical formulas are done away with completely, while the logicbased approach uses the system of modal logic.
Typically, the logicbased approach has been used in fields such as philosophy, logic and AI, while the eventbased approach is more often used in fields such as game theory and mathematical economics. In the logicbased approach, a syntax and semantics have been built using the language of modal logic, which we will now describe.
Syntax
The basic modal operator of epistemic logic, usually written K, can be read as "it is known that," "it is epistemically necessary that," or "it is inconsistent with what is known that not." If there is more than one agent whose knowledge is to be represented, subscripts can be attached to the operator (\mathit{K}_1, \mathit{K}_2, etc.) to indicate which agent one is talking about. So \mathit{K}_a\varphi can be read as "Agent a knows that \varphi." Thus, epistemic logic can be an example of multimodal logic applied for knowledge representation.^{[1]} The dual of K, which would be in the same relationship to K as \Diamond is to \Box, has no specific symbol, but can be represented by \neg K_a \neg \varphi, which can be read as "a does not know that not \varphi" or "It is consistent with a's knowledge that \varphi is possible". The statement "a does not know whether or not \varphi" can be expressed as \neg K_a\varphi \land \neg K_a\neg\varphi.
In order to accommodate notions of common knowledge and distributed knowledge, three other modal operators can be added to the language. These are \mathit{E}_\mathit{G}, which reads "every agent in group G knows;" \mathit{C}_\mathit{G}, which reads "it is common knowledge to every agent in G;" and \mathit{D}_\mathit{G}, which reads "it is distributed knowledge to every agent in G." If \varphi is a formula of our language, then so are \mathit{E}_G \varphi, \mathit{C}_G \varphi, and \mathit{D}_G \varphi. Just as the subscript after \mathit{K} can be omitted when there is only one agent, the subscript after the modal operators \mathit{E}, \mathit{C}, and \mathit{D} can be omitted when the group is the set of all agents.
Semantics
As we mentioned above, the logicbased approach is built upon the possible worlds model, the semantics of which are often given definite form in Kripke structures, also known as Kripke models. A Kripke structure M for n agents over \Phi is a triple (S, \pi, \mathcal{K}_1, ..., \mathcal{K}_n), where S is a nonempty set of states or possible worlds, \pi is an interpretation which associates with each state in S a truth assignment to the primitive propositions in \Phi, and \mathcal{K}_1, ..., \mathcal{K}_n are binary relations on S for n numbers of agents. It is important here not to confuse K_i, our modal operator, and \mathcal{K}_i, our accessibility relation.
The truth assignment tells us whether or not a proposition p is true or false in a certain state. So \pi (s)(p) tells us whether p is true in state s in model \mathcal{M}. Truth depends not only on the structure, but on the current world as well. Just because something is true in one world does not mean it is true in another. To state that a formula \varphi is true at a certain world, one writes (M,s) \models \varphi, normally read as "\varphi is true at (M,s)," or "(M,s) satisfies \varphi".
It is useful to think of our binary relation \mathcal{K}_i as a possibility relation, because it is meant to capture what worlds or states agent i considers to be possible. In idealized accounts of knowledge (e.g., describing the epistemic status of perfect reasoners with infinite memory capacity), it makes sense for \mathcal{K}_i to be an equivalence relation, since this is the strongest form and is the most appropriate for the greatest number of applications. An equivalence relation is a binary relation that is reflexive, symmetric, and transitive. The accessibility relation does not have to have these qualities; there are certainly other choices possible, such as those used when modeling belief rather than knowledge.
The properties of knowledge
Assuming that \mathcal{K}_i is an equivalence relation, and that the agents are perfect reasoners, a few properties of knowledge can be derived. The properties listed here are often known as the "S5 Properties," for reasons described in the Axiom Systems section below.
The distribution axiom
This axiom is traditionally known as K. In epistemic terms, it states that if an agent knows \varphi and knows that \varphi \implies \psi, then the agent must also know \,\psi. So,

(K_i\varphi \land K_i(\varphi \implies \psi)) \implies K_i\psi
The knowledge generalization rule
Another property we can derive is that if \phi is valid, then K_i\phi. It does not mean that if \phi is true, that agent i knows \phi. What it means is that if \phi is true in every world that an agent considers to be a possible world, then the agent must know \phi at every possible world. This principle is traditionally called N.

\text{if }M \models \varphi\text{ then }M \models K_i \varphi.\,
The knowledge or truth axiom
This axiom is also known as T. It says that if an agent knows facts, the facts must be true. This has often been taken as the major distinguishing feature between knowledge and belief. We can believe a statement to be true when it is false, but it would be impossible to know a false statement.

K_i \varphi \implies \varphi
The positive introspection axiom
This property and the next state that an agent has introspection about its own knowledge, and are traditionally known as 4 and 5, respectively. The Positive Introspection Axiom, also known as the KK Axiom, says specifically that agents know that they know what they know. This axiom may seem less obvious than the ones listed previously, and Timothy Williamson has argued against its inclusion forcefully in his book, Knowledge and Its Limits.

K_i \varphi \implies K_i K_i \varphi
The negative introspection axiom
The Negative Introspection Axiom says that agents know that they do not know what they do not know.

\neg K_i \varphi \implies K_i \neg K_i \varphi
Axiom systems
Different modal logics can be derived from taking different subsets of these axioms, and these logics are normally named after the important axioms being employed. However, this is not always the case. KT45, the modal logic that results from the combining of K, T, 4, 5, and the Knowledge Generalization Rule, is primarily known as S5. This is why the properties of knowledge described above are often called the S5 Properties.
Epistemic logic also deals with belief, not just knowledge. The basic modal operator is usually written B instead of K. In this case though, the knowledge axiom no longer seems right—agents only sometimes believe the truth—so it is usually replaced with the Consistency Axiom, traditionally called D:

\neg B_i \bot
which states that the agent does not believe a contradiction, or that which is false. When D replaces T in S5, the resulting system is known as KD45. This results in different properties for \mathcal{K}_i as well. For example, in a system where an agent "believes" something to be true, but it is not actually true, the accessibility relation would be nonreflexive. The logic of belief is called doxastic logic.
Problems with the possible world model and modal model of knowledge
If we take the possible worlds approach to knowledge, it follows that our epistemic agent a knows all the logical consequences of his or her or its beliefs. If Q is a logical consequence of P, then there is no possible world where P is true but Q is not. So if a knows that P, it follows that all of the logical consequences of P are true of all of the possible worlds compatible with a 's beliefs. Therefore, a knows Q. It is not epistemically possible for a that notQ given his knowledge that P. This consideration was a part of what led Robert Stalnaker to develop two dimensionalism, which can arguably explain how we might not know all the logical consequences of our beliefs even if there are no worlds where the propositions we know come out true but their consequences false.^{[2]}
Even when we ignore possible world semantics and stick to axiomatic systems, this peculiar feature holds. With K and N (the Distribution Rule and the Knowledge Generalization Rule, respectively), which are axioms that are minimally true of all normal modal logics, we can prove that we know all the logical consequences of our beliefs. If Q is a logical consequence of P, then we can derive \mathcal{K}_a (P \rightarrow Q) with N and the conditional proof and then \mathcal{K}_a P \rightarrow \mathcal{K}_a Q with K. When we translate this into epistemic terms, this says that if Q is a logical consequence of P, then a knows that it is, and if a knows P, a knows Q. That is to say, a knows all the logical consequences of every proposition. This is necessarily true of all classical modal logics. But then, for example, if a knows that prime numbers are divisible only by themselves and the number one, then a knows that 8683317618811886495518194401279999999 is prime (since this number is only divisible by itself and the number one). That is to say, under the modal interpretation of knowledge, when a knows the definition of a prime number, a knows that this number is prime. It should be clear at this point that a is not human. This shows that epistemic modal logic is an idealized account of knowledge, and explains objective, rather than subjective knowledge (if anything).^{[3]}
See also
Notes

^ p. 257 in: Ferenczi, Miklós (2002). Matematikai logika (in Hungarian). Budapest: Műszaki könyvkiadó. ISBN .

257

^ Stalnaker, Robert. "Propositions." Issues in the Philosophy of Language. Yale UP, 1976. p. 101.

^ See Ted Sider's Logic for Philosophy. Currently page 230 but subject to change following updates.
References

Anderson, A. and N. D. Belnap. Entailment: The Logic of Relevance and Necessity. Princeton: Princeton University Press, 1975. ASIN B001NNPJL8.

Fagin, Ronald; Halpern, Joseph; Moses, Yoram; Vardi, Moshe (2003). Reasoning about Knowledge. Cambridge: MIT Press. ISBN . . A classic reference.

Ronald Fagin, Joseph Halpern, Moshe Vardi. "A nonstandard approach to the logical omniscience problem." Artificial Intelligence, Volume 79, Number 2, 1995, p. 20340.

Hendricks, V.F. Mainstream and Formal Epistemology. New York: Cambridge University Press, 2007.

Hintikka, Jaakko (1962). Knowledge and Belief  An Introduction to the Logic of the Two Notions. Ithaca: Cornell University Press. ISBN . .

Meyer, JJ C., 2001, "Epistemic Logic," in Goble, Lou, ed., The Blackwell Guide to Philosophical Logic. Blackwell.

Montague, R. "Universal Grammar". Theoretica, Volume 36, 1970, p. 373398.

Rescher, Nicolas (2005). Epistemic Logic: A Survey Of the Logic Of Knowledge. University of Pittsburgh Press. ISBN . .

Shoham, Yoav; LeytonBrown, Kevin (2009). Multiagent Systems: Algorithmic, GameTheoretic, and Logical Foundations. New York: Cambridge University Press. ISBN . . See Chapters 13 and 14; downloadable free online.
External links
This article was sourced from Creative Commons AttributionShareAlike License; additional terms may apply. World Heritage Encyclopedia content is assembled from numerous content providers, Open Access Publishing, and in compliance with The Fair Access to Science and Technology Research Act (FASTR), Wikimedia Foundation, Inc., Public Library of Science, The Encyclopedia of Life, Open Book Publishers (OBP), PubMed, U.S. National Library of Medicine, National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health (NIH), U.S. Department of Health & Human Services, and USA.gov, which sources content from all federal, state, local, tribal, and territorial government publication portals (.gov, .mil, .edu). Funding for USA.gov and content contributors is made possible from the U.S. Congress, EGovernment Act of 2002.
Crowd sourced content that is contributed to World Heritage Encyclopedia is peer reviewed and edited by our editorial staff to ensure quality scholarly research articles.
By using this site, you agree to the Terms of Use and Privacy Policy. World Heritage Encyclopedia™ is a registered trademark of the World Public Library Association, a nonprofit organization.