2,823 research outputs found
Modeling Belief in Dynamic Systems, Part II: Revision and Update
The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper (Friedman & Halpern,
1997), we introduce a new framework to model belief change. This framework
combines temporal and epistemic modalities with a notion of plausibility,
allowing us to examine the change of beliefs over time. In this paper, we show
how belief revision and belief update can be captured in our framework. This
allows us to compare the assumptions made by each method, and to better
understand the principles underlying them. In particular, it shows that Katsuno
and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on
several strong assumptions that may limit its applicability in artificial
intelligence. Finally, our analysis allow us to identify a notion of minimal
change that underlies a broad range of belief change operations including
revision and update.Comment: See http://www.jair.org/ for other files accompanying this articl
Efficiency and Nash Equilibria in a Scrip System for P2P Networks
A model of providing service in a P2P network is analyzed. It is shown that
by adding a scrip system, a mechanism that admits a reasonable Nash equilibrium
that reduces free riding can be obtained. The effect of varying the total
amount of money (scrip) in the system on efficiency (i.e., social welfare) is
analyzed, and it is shown that by maintaining the appropriate ratio between the
total amount of money and the number of agents, efficiency is maximized. The
work has implications for many online systems, not only P2P networks but also a
wide variety of online forums for which scrip systems are popular, but formal
analyses have been lacking
Defining Relative Likelihood in Partially-Ordered Preferential Structures
Starting with a likelihood or preference order on worlds, we extend it to a
likelihood ordering on sets of worlds in a natural way, and examine the
resulting logic. Lewis earlier considered such a notion of relative likelihood
in the context of studying counterfactuals, but he assumed a total preference
order on worlds. Complications arise when examining partial orders that are not
present for total orders. There are subtleties involving the exact approach to
lifting the order on worlds to an order on sets of worlds. In addition, the
axiomatization of the logic of relative likelihood in the case of partial
orders gives insight into the connection between relative likelihood and
default reasoning.Comment: See http://www.jair.org/ for any accompanying file
Probabilistic Algorithmic Knowledge
The framework of algorithmic knowledge assumes that agents use deterministic
knowledge algorithms to compute the facts they explicitly know. We extend the
framework to allow for randomized knowledge algorithms. We then characterize
the information provided by a randomized knowledge algorithm when its answers
have some probability of being incorrect. We formalize this information in
terms of evidence; a randomized knowledge algorithm returning ``Yes'' to a
query about a fact \phi provides evidence for \phi being true. Finally, we
discuss the extent to which this evidence can be used as a basis for decisions.Comment: 26 pages. A preliminary version appeared in Proc. 9th Conference on
Theoretical Aspects of Rationality and Knowledge (TARK'03
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