66,957 research outputs found
The KB paradigm and its application to interactive configuration
The knowledge base paradigm aims to express domain knowledge in a rich formal
language, and to use this domain knowledge as a knowledge base to solve various
problems and tasks that arise in the domain by applying multiple forms of
inference. As such, the paradigm applies a strict separation of concerns
between information and problem solving. In this paper, we analyze the
principles and feasibility of the knowledge base paradigm in the context of an
important class of applications: interactive configuration problems. In
interactive configuration problems, a configuration of interrelated objects
under constraints is searched, where the system assists the user in reaching an
intended configuration. It is widely recognized in industry that good software
solutions for these problems are very difficult to develop. We investigate such
problems from the perspective of the KB paradigm. We show that multiple
functionalities in this domain can be achieved by applying different forms of
logical inferences on a formal specification of the configuration domain. We
report on a proof of concept of this approach in a real-life application with a
banking company. To appear in Theory and Practice of Logic Programming (TPLP).Comment: To appear in Theory and Practice of Logic Programming (TPLP
Distributed Computing with Adaptive Heuristics
We use ideas from distributed computing to study dynamic environments in
which computational nodes, or decision makers, follow adaptive heuristics (Hart
2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly
"best replying" to others' actions, and minimizing "regret", that have been
extensively studied in game theory and economics. We explore when convergence
of such simple dynamics to an equilibrium is guaranteed in asynchronous
computational environments, where nodes can act at any time. Our research
agenda, distributed computing with adaptive heuristics, lies on the borderline
of computer science (including distributed computing and learning) and game
theory (including game dynamics and adaptive heuristics). We exhibit a general
non-termination result for a broad class of heuristics with bounded
recall---that is, simple rules of behavior that depend only on recent history
of interaction between nodes. We consider implications of our result across a
wide variety of interesting and timely applications: game theory, circuit
design, social networks, routing and congestion control. We also study the
computational and communication complexity of asynchronous dynamics and present
some basic observations regarding the effects of asynchrony on no-regret
dynamics. We believe that our work opens a new avenue for research in both
distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion
of v1. Revised version will appear in the proceedings of Innovations in
Computer Science 201
Cellular Systems with Many Antennas: Large System Analysis under Pilot Contamination
Base stations with a large number of transmit antennas have the potential to
serve a large number of users simultaneously at higher rates. They also promise
a lower power consumption due to coherent combining at the receiver. However,
the receiver processing in the uplink relies on the channel estimates which are
known to suffer from pilot interference. In this work, we perform an uplink
large system analysis of multi-cell multi-antenna system when the receiver
employs a matched filtering with a pilot contaminated estimate. We find the
asymptotic Signal to Interference plus Noise Ratio (SINR) as the number of
antennas and number of users per base station grow large while maintaining a
fixed ratio. To do this, we make use of the similarity of the uplink received
signal in a multi-antenna system to the representation of the received signal
in CDMA systems. The asymptotic SINR expression explicitly captures the effect
of pilot contamination and that of interference averaging. This also explains
the SINR performance of receiver processing schemes at different regimes such
as instances when the number of antennas are comparable to number of users as
well as when antennas exceed greatly the number of users. Finally, we also
propose that the adaptive MMSE symbol detection scheme, which does not require
the explicit channel knowledge, can be employed for cellular systems with large
number of antennas.Comment: 5 pages, 4 figure
Caching with Partial Adaptive Matching
We study the caching problem when we are allowed to match each user to one of
a subset of caches after its request is revealed. We focus on non-uniformly
popular content, specifically when the file popularities obey a Zipf
distribution. We study two extremal schemes, one focusing on coded server
transmissions while ignoring matching capabilities, and the other focusing on
adaptive matching while ignoring potential coding opportunities. We derive the
rates achieved by these schemes and characterize the regimes in which one
outperforms the other. We also compare them to information-theoretic outer
bounds, and finally propose a hybrid scheme that generalizes ideas from the two
schemes and performs at least as well as either of them in most memory regimes.Comment: 35 pages, 7 figures. Shorter versions have appeared in IEEE ISIT 2017
and IEEE ITW 201
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