Monday April 22nd 2013
Philosophy Department, RUC
starting at 13:00.
13.00 Henning Christiansen:
Abductive reasoning made easy with logic programming with constraints (Abstract below)
13.45 Kevin Kelly:
A Learning Semantics for Inductive Knowledge (Abstract below)
14.30 Coffee Break
14.45 Sonja Smets and Alexandru Baltag:
Paradox, Belief Change and Fixed Points:
or How to Avoid Unexpected Exams (Abstract below)
15.45 Wine
The four speakers are:
Kevin Kelly -
A Learning Semantics for Inductive Knowledge
ABSTRACT:
Knowledge evidently has something to do with inquiry: the evolution of belief in light of increasing information. Wouldn’t it be nice if the modal semantics of epistemic logic included all of those obvious components, along with an account of what inductive knowledge is? Then its models would not merely represent what is knowable---they would explain how it is knowable. They would also explain whether and how one can know what is not learnable, know that one knows, know that one doesn’t know or know the consequences of what one knows. They could also explain how teachers can transfer scientific knowledge to gullible students and how a kernel of scientific knowledge can erupt into common scientific knowledge in a gullible community. One of the leading ideas in the semantics is that inductive learnability is not necessary for inductive knowability. Knowledge allows for serendipitous luck in selecting a learnable reason for believing a theory that is not itself learnable. The talk builds upon discussions decades ago with Stig Andur Pedersen and Vincent Hendricks, so it is a pleasure to present it at Roskilde.
Henning Christiansen
Abductive reasoning made easy with logic programming with constraints
ABSTRACT:
Abductive reasoning, or "abduction", means to find a best explanation for some unexpected observation. In a logical setting, an explanation can be a set of facts which, when added to our current knowledge base, makes it possible to prove the truth of the observation and, at the same time, is not inconsistent with the knowledge base.
Introduced by Peirce, the notion has attracted much attention in philosophy, detective stories and computer science, most notably in logic programming. Until the shift of the millennium, abduction in logic programming was realized through complex meta-interpreters written in Prolog, which may have led to a view of abduction as being some hairy, difficult stuff, far too inefficient for any realistic applications. In this talk, we demonstrate how a fairly powerful version abductive reasoning can be exercised through a direct use of Prolog, using its extension by Constraint Handling Rules as the engine to take care of abducible hypotheses.
Sonja Smets & Alexandru Baltag
Paradox, Belief Change and Fixed Points:
or How to Avoid Unexpected Exams
ABSTRACT:
We develop a theory of dynamic-doxastic attitudes, modeling in a qualitative manner various forms of trust/distrust in the reliability of a source of information. Formally, such attitudes correspond to doxastic transformers, i.e. ways of changing doxastic/plausibility models with any new piece of information originating in the given source. We argue that the fixed points of these transformers play a central role in solving various epistemic puzzles, such as the Surprise Exam Paradox
Philosophy Department, RUC
starting at 13:00.
13.00 Henning Christiansen:
Abductive reasoning made easy with logic programming with constraints (Abstract below)
13.45 Kevin Kelly:
A Learning Semantics for Inductive Knowledge (Abstract below)
14.30 Coffee Break
14.45 Sonja Smets and Alexandru Baltag:
Paradox, Belief Change and Fixed Points:
or How to Avoid Unexpected Exams (Abstract below)
15.45 Wine
The four speakers are:
Kevin Kelly -
A Learning Semantics for Inductive Knowledge
ABSTRACT:
Knowledge evidently has something to do with inquiry: the evolution of belief in light of increasing information. Wouldn’t it be nice if the modal semantics of epistemic logic included all of those obvious components, along with an account of what inductive knowledge is? Then its models would not merely represent what is knowable---they would explain how it is knowable. They would also explain whether and how one can know what is not learnable, know that one knows, know that one doesn’t know or know the consequences of what one knows. They could also explain how teachers can transfer scientific knowledge to gullible students and how a kernel of scientific knowledge can erupt into common scientific knowledge in a gullible community. One of the leading ideas in the semantics is that inductive learnability is not necessary for inductive knowability. Knowledge allows for serendipitous luck in selecting a learnable reason for believing a theory that is not itself learnable. The talk builds upon discussions decades ago with Stig Andur Pedersen and Vincent Hendricks, so it is a pleasure to present it at Roskilde.
Henning Christiansen
Abductive reasoning made easy with logic programming with constraints
ABSTRACT:
Abductive reasoning, or "abduction", means to find a best explanation for some unexpected observation. In a logical setting, an explanation can be a set of facts which, when added to our current knowledge base, makes it possible to prove the truth of the observation and, at the same time, is not inconsistent with the knowledge base.
Introduced by Peirce, the notion has attracted much attention in philosophy, detective stories and computer science, most notably in logic programming. Until the shift of the millennium, abduction in logic programming was realized through complex meta-interpreters written in Prolog, which may have led to a view of abduction as being some hairy, difficult stuff, far too inefficient for any realistic applications. In this talk, we demonstrate how a fairly powerful version abductive reasoning can be exercised through a direct use of Prolog, using its extension by Constraint Handling Rules as the engine to take care of abducible hypotheses.
Sonja Smets & Alexandru Baltag
Paradox, Belief Change and Fixed Points:
or How to Avoid Unexpected Exams
ABSTRACT:
We develop a theory of dynamic-doxastic attitudes, modeling in a qualitative manner various forms of trust/distrust in the reliability of a source of information. Formally, such attitudes correspond to doxastic transformers, i.e. ways of changing doxastic/plausibility models with any new piece of information originating in the given source. We argue that the fixed points of these transformers play a central role in solving various epistemic puzzles, such as the Surprise Exam Paradox
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