knowledge. Probabilistic semantics extend the current semantic technology to overcome that limitation. However, due to their probabilistic approach, probabilistic...

at NYU. His Cambridge dissertation explored the foundations of probabilistic semantics. In 1992, Appiah published In My Father's House, which won the...

logic programming are based on the distribution semantics, which splits a program into a set of probabilistic facts and a logic program. It defines a probability...

ISBN 0-262-03293-7. Chapter 5: Probabilistic Analysis and Randomized Algorithms, pp. 91–122. Dirk Draheim. "Semantics of the Probabilistic Typed Lambda Calculus...

quantifiers. Probabilistic semantics originated from Hartry Field and has been shown equivalent to and a natural generalization of truth-value semantics. Like...

the conversational common ground. Probabilistic approaches motivated by gradable modal expressions provide a semantics which appeals to speaker credence...

logic programming are based on the distribution semantics, which splits a program into a set of probabilistic facts and a logic program. It defines a probability...

Christoph (2022-05-31). Probabilistic Databases. Springer Nature. ISBN 978-3-031-01879-4. See section 1.2.2, "Possible Worlds Semantics" Lewis, David K. (1973)...

building out the parse tree using a probabilistic context-free grammar (PCFG) (see also stochastic grammar). Lexical semantics What is the computational meaning...

semantics, and natural language understanding. Potts's research centers on formal semantics and pragmatic reasoning. He has developed probabilistic extensions...

true or false. It is a central concept in the philosophy of language, semantics, logic, and related fields. Propositions are the objects denoted by declarative...

distributed computation, probabilistic computation, quantum computation, automata theory, information theory, cryptography, program semantics and verification...

but with different semantics than in imprecise probability theory. In addition to the interpretation of truth in a probabilistic fashion, a truth value...

Predicate transformer semantics were introduced by Edsger Dijkstra in his seminal paper "Guarded commands, nondeterminacy and formal derivation of programs"...

communication. Theories of pragmatics go hand-in-hand with theories of semantics, which studies aspects of meaning, and syntax, which examines sentence...

category mistake, it was intended to show the inadequacy of certain probabilistic models of grammar, and the need for more structured models. Chomsky...

to determine pricing and make trading decisions. Governments apply probabilistic methods in environmental regulation, entitlement analysis, and financial...

Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional...

in ACE) ProGolem Probabilistic inductive logic programming adapts the setting of inductive logic programming to learning probabilistic logic programs....

engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit...

and lack of knowledge about facts. The stable model semantics, which is used to give a semantics to logic programming with negation as failure, can be...

set of probabilistic facts F {\displaystyle {\mathcal {F}}} and a set of rules R {\displaystyle {\mathcal {R}}} . Using the distribution semantics, a probability...

are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA). Latent Dirichlet allocation, which...

concerned with trying to develop a logical semantics for negation as failure and with developing other semantics and other implementations for negation....

and semantics. The syntactic rules of a formal system determine how to deduce conclusions from premises, i.e. how to formulate proofs. The semantics of...

Intelligence. Riguzzi, Fabrizio (2023). Foundations of probabilistic logic programming: Languages, semantics, inference and learning (2nd ed.). Gistrup, Denmark:...

Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. It is applicable...

networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation...

chaining. There are also examples of probabilistic reasoners, including non-axiomatic reasoning systems, and probabilistic logic networks. Notable semantic...

situation. It is a generalization of a Bayesian network, in which not only probabilistic inference problems but also decision making problems (following the...