148,165 research outputs found
A group learning management method for intelligent tutoring systems
In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture
Basic protocols in quantum reinforcement learning with superconducting circuits
Superconducting circuit technologies have recently achieved quantum protocols
involving closed feedback loops. Quantum artificial intelligence and quantum
machine learning are emerging fields inside quantum technologies which may
enable quantum devices to acquire information from the outer world and improve
themselves via a learning process. Here we propose the implementation of basic
protocols in quantum reinforcement learning, with superconducting circuits
employing feedback-loop control. We introduce diverse scenarios for
proof-of-principle experiments with state-of-the-art superconducting circuit
technologies and analyze their feasibility in presence of imperfections. The
field of quantum artificial intelligence implemented with superconducting
circuits paves the way for enhanced quantum control and quantum computation
protocols.Comment: Published versio
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Using touch devices to navigate in virtual 3D environments such as computer
assisted design (CAD) models or geographical information systems (GIS) is
inherently difficult for humans, as the 3D operations have to be performed by
the user on a 2D touch surface. This ill-posed problem is classically solved
with a fixed and handcrafted interaction protocol, which must be learned by the
user. We propose to automatically learn a new interaction protocol allowing to
map a 2D user input to 3D actions in virtual environments using reinforcement
learning (RL). A fundamental problem of RL methods is the vast amount of
interactions often required, which are difficult to come by when humans are
involved. To overcome this limitation, we make use of two collaborative agents.
The first agent models the human by learning to perform the 2D finger
trajectories. The second agent acts as the interaction protocol, interpreting
and translating to 3D operations the 2D finger trajectories from the first
agent. We restrict the learned 2D trajectories to be similar to a training set
of collected human gestures by first performing state representation learning,
prior to reinforcement learning. This state representation learning is
addressed by projecting the gestures into a latent space learned by a
variational auto encoder (VAE).Comment: 17 pages, 8 figures. Accepted at The European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases 2019
(ECMLPKDD 2019
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Representing Conversations for Scalable Overhearing
Open distributed multi-agent systems are gaining interest in the academic
community and in industry. In such open settings, agents are often coordinated
using standardized agent conversation protocols. The representation of such
protocols (for analysis, validation, monitoring, etc) is an important aspect of
multi-agent applications. Recently, Petri nets have been shown to be an
interesting approach to such representation, and radically different approaches
using Petri nets have been proposed. However, their relative strengths and
weaknesses have not been examined. Moreover, their scalability and suitability
for different tasks have not been addressed. This paper addresses both these
challenges. First, we analyze existing Petri net representations in terms of
their scalability and appropriateness for overhearing, an important task in
monitoring open multi-agent systems. Then, building on the insights gained, we
introduce a novel representation using Colored Petri nets that explicitly
represent legal joint conversation states and messages. This representation
approach offers significant improvements in scalability and is particularly
suitable for overhearing. Furthermore, we show that this new representation
offers a comprehensive coverage of all conversation features of FIPA
conversation standards. We also present a procedure for transforming AUML
conversation protocol diagrams (a standard human-readable representation), to
our Colored Petri net representation
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