42,083 research outputs found
Strategy Logic with Imperfect Information
We introduce an extension of Strategy Logic for the imperfect-information
setting, called SLii, and study its model-checking problem. As this logic
naturally captures multi-player games with imperfect information, the problem
turns out to be undecidable. We introduce a syntactical class of "hierarchical
instances" for which, intuitively, as one goes down the syntactic tree of the
formula, strategy quantifications are concerned with finer observations of the
model. We prove that model-checking SLii restricted to hierarchical instances
is decidable. This result, because it allows for complex patterns of
existential and universal quantification on strategies, greatly generalises
previous ones, such as decidability of multi-player games with imperfect
information and hierarchical observations, and decidability of distributed
synthesis for hierarchical systems. To establish the decidability result, we
introduce and study QCTL*ii, an extension of QCTL* (itself an extension of CTL*
with second-order quantification over atomic propositions) by parameterising
its quantifiers with observations. The simple syntax of QCTL* ii allows us to
provide a conceptually neat reduction of SLii to QCTL*ii that separates
concerns, allowing one to forget about strategies and players and focus solely
on second-order quantification. While the model-checking problem of QCTL*ii is,
in general, undecidable, we identify a syntactic fragment of hierarchical
formulas and prove, using an automata-theoretic approach, that it is decidable.
The decidability result for SLii follows since the reduction maps hierarchical
instances of SLii to hierarchical formulas of QCTL*ii
Solving Imperfect Information Games Using Decomposition
Decomposition, i.e. independently analyzing possible subgames, has proven to
be an essential principle for effective decision-making in perfect information
games. However, in imperfect information games, decomposition has proven to be
problematic. To date, all proposed techniques for decomposition in imperfect
information games have abandoned theoretical guarantees. This work presents the
first technique for decomposing an imperfect information game into subgames
that can be solved independently, while retaining optimality guarantees on the
full-game solution. We can use this technique to construct theoretically
justified algorithms that make better use of information available at run-time,
overcome memory or disk limitations at run-time, or make a time/space trade-off
to overcome memory or disk limitations while solving a game. In particular, we
present an algorithm for subgame solving which guarantees performance in the
whole game, in contrast to existing methods which may have unbounded error. In
addition, we present an offline game solving algorithm, CFR-D, which can
produce a Nash equilibrium for a game that is larger than available storage.Comment: 7 pages by 2 columns, 5 figures; April 21 2014 - expand explanations
and theor
New results on pushdown module checking with imperfect information
Model checking of open pushdown systems (OPD) w.r.t. standard branching
temporal logics (pushdown module checking or PMC) has been recently
investigated in the literature, both in the context of environments with
perfect and imperfect information about the system (in the last case, the
environment has only a partial view of the system's control states and stack
content). For standard CTL, PMC with imperfect information is known to be
undecidable. If the stack content is assumed to be visible, then the problem is
decidable and 2EXPTIME-complete (matching the complexity of PMC with perfect
information against CTL). The decidability status of PMC with imperfect
information against CTL restricted to the case where the depth of the stack
content is visible is open. In this paper, we show that with this restriction,
PMC with imperfect information against CTL remains undecidable. On the other
hand, we individuate an interesting subclass of OPDS with visible stack content
depth such that PMC with imperfect information against the existential fragment
of CTL is decidable and in 2EXPTIME. Moreover, we show that the program
complexity of PMC with imperfect information and visible stack content against
CTL is 2EXPTIME-complete (hence, exponentially harder than the program
complexity of PMC with perfect information, which is known to be
EXPTIME-complete).Comment: In Proceedings GandALF 2011, arXiv:1106.081
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
first a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
Distributed Stochastic Optimization under Imperfect Information
We consider a stochastic convex optimization problem that requires minimizing
a sum of misspecified agentspecific expectation-valued convex functions over
the intersection of a collection of agent-specific convex sets. This
misspecification is manifested in a parametric sense and may be resolved
through solving a distinct stochastic convex learning problem. Our interest
lies in the development of distributed algorithms in which every agent makes
decisions based on the knowledge of its objective and feasibility set while
learning the decisions of other agents by communicating with its local
neighbors over a time-varying connectivity graph. While a significant body of
research currently exists in the context of such problems, we believe that the
misspecified generalization of this problem is both important and has seen
little study, if at all. Accordingly, our focus lies on the simultaneous
resolution of both problems through a joint set of schemes that combine three
distinct steps: (i) An alignment step in which every agent updates its current
belief by averaging over the beliefs of its neighbors; (ii) A projected
(stochastic) gradient step in which every agent further updates this averaged
estimate; and (iii) A learning step in which agents update their belief of the
misspecified parameter by utilizing a stochastic gradient step. Under an
assumption of mere convexity on agent objectives and strong convexity of the
learning problems, we show that the sequences generated by this collection of
update rules converge almost surely to the solution of the correctly specified
stochastic convex optimization problem and the stochastic learning problem,
respectively
Imperfect Information, Democracy, and Populism
The modern world is complex and difficult to understand for voters, who may hold beliefs that are at variance with reality. Politicians face incentives to pander to voters' beliefs to get reelected. We analyze the welfare effects of this pandering and show that it entails both costs and benefits. Moreover, we explore optimal constitutional design in the presence of imperfect information about how the world works. We compare indirect democracy to direct democracy and to delegation of policy making to independent agents. We find that indirect democracy is often welfare maximizing.Imperfect information;beliefs;democracy;populism;accountabil- ity;experts
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