Search Results for author:
Found 5 papers, 0 papers with code
Thinking About Causation: A Causal Language with Epistemic Operators
To this purpose, we extend the notion of a causal model with a representation of the epistemic state of an agent.
Learning Probabilities: Towards a Logic of Statistical Learning
The most widespread model for such situations of 'radical uncertainty' is in terms of imprecise probabilities, i. e. representing the agent's knowledge as a set of probability measures.
Argument-based Belief in Topological Structures
In our combined setting, we use a topological semantics to represent the structure of an agent's collection of evidence, and we use argumentation theory to single out the relevant sets of evidence through which a notion of beliefs grounded on arguments is defined.
On the Solvability of Inductive Problems: A Study in Epistemic Topology
We investigate the issues of inductive problem-solving and learning by doxastic agents.
Revisable Justified Belief: Preliminary Report
We introduce a theory $\mathsf{JCDL}$ of Justified Conditional Doxastic Logic that replaces conditional belief formulas $B^\psi\varphi$ by expressions $t{\,:^{\psi}}\varphi$ made up of a term $t$ whose syntactic structure suggests a derivation of the belief $\varphi$ after revision by $\psi$.
