Simulative Inference and the Semantics of Belief
Interacting effectively with another agent, whether in competition or
in cooperation, often requires reasoning about that agent's mental
state. We can get clues about others' mental states by observing
their behavior, or by attributing attitudes we know are common to all
agents, but sometimes that's not enough. Sometimes we must draw
inferences based on an understanding of how others think. An exact
model of another agent's reasoning mechanism is rarely available, but
when the agent is similar to us, we have a good approximation: our own
reasoning ability. By temporarily assuming the attitudes we attribute
to another agent, and then observing where our own reasoning process
leads, we can sometimes draw further conclusions about the other
agent's mental state. To draw conclusions in this way is to perform
simulative reasoning.
Our work on simulative inference consists of two parts: developing a
logical representation for reasoning about agents' mental states, with
a formal semantics that lends itself to a treatment of simulation; and
implementing extensions to Epilog, a
knowledge representation and reasoning system, that demonstrate the
use of simulative reasoning in natural language understanding.
This work was the thesis project of Aaron
Kaplan, supervised by Len Schubert.
Aaron
Kaplan's home page
Research in CS at U of R