9,241 research outputs found
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review
The Human Activity Recognition (HAR) tasks automatically identify human
activities using the sensor data, which has numerous applications in
healthcare, sports, security, and human-computer interaction. Despite
significant advances in HAR, critical challenges still exist. Game theory has
emerged as a promising solution to address these challenges in machine learning
problems including HAR. However, there is a lack of research work on applying
game theory solutions to the HAR problems. This review paper explores the
potential of game theory as a solution for HAR tasks, and bridges the gap
between game theory and HAR research work by suggesting novel game-theoretic
approaches for HAR problems. The contributions of this work include exploring
how game theory can improve the accuracy and robustness of HAR models,
investigating how game-theoretic concepts can optimize recognition algorithms,
and discussing the game-theoretic approaches against the existing HAR methods.
The objective is to provide insights into the potential of game theory as a
solution for sensor-based HAR, and contribute to develop a more accurate and
efficient recognition system in the future research directions
A Non-Cooperative Game Theoretical Approach For Power Control In Virtual MIMO Wireless Sensor Network
Power management is one of the vital issue in wireless sensor networks, where
the lifetime of the network relies on battery powered nodes. Transmitting at
high power reduces the lifetime of both the nodes and the network. One
efficient way of power management is to control the power at which the nodes
transmit. In this paper, a virtual multiple input multiple output wireless
sensor network (VMIMO-WSN)communication architecture is considered and the
power control of sensor nodes based on the approach of game theory is
formulated. The use of game theory has proliferated, with a broad range of
applications in wireless sensor networking. Approaches from game theory can be
used to optimize node level as well as network wide performance. The game here
is categorized as an incomplete information game, in which the nodes do not
have complete information about the strategies taken by other nodes. For
virtual multiple input multiple output wireless sensor network architecture
considered, the Nash equilibrium is used to decide the optimal power level at
which a node needs to transmit, to maximize its utility. Outcome shows that the
game theoretic approach considered for VMIMO-WSN architecture achieves the best
utility, by consuming less power.Comment: 12 pages, 8 figure
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence
An important challenge for safety in machine learning and artificial
intelligence systems is a~set of related failures involving specification
gaming, reward hacking, fragility to distributional shifts, and Goodhart's or
Campbell's law. This paper presents additional failure modes for interactions
within multi-agent systems that are closely related. These multi-agent failure
modes are more complex, more problematic, and less well understood than the
single-agent case, and are also already occurring, largely unnoticed. After
motivating the discussion with examples from poker-playing artificial
intelligence (AI), the paper explains why these failure modes are in some
senses unavoidable. Following this, the paper categorizes failure modes,
provides definitions, and cites examples for each of the modes: accidental
steering, coordination failures, adversarial misalignment, input spoofing and
filtering, and goal co-option or direct hacking. The paper then discusses how
extant literature on multi-agent AI fails to address these failure modes, and
identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive
Computing, Special Issue "Artificial Superintelligence: Coordination &
Strategy
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