148,395 research outputs found
Massively parallel computing on an organic molecular layer
Current computers operate at enormous speeds of ~10^13 bits/s, but their
principle of sequential logic operation has remained unchanged since the 1950s.
Though our brain is much slower on a per-neuron base (~10^3 firings/s), it is
capable of remarkable decision-making based on the collective operations of
millions of neurons at a time in ever-evolving neural circuitry. Here we use
molecular switches to build an assembly where each molecule communicates-like
neurons-with many neighbors simultaneously. The assembly's ability to
reconfigure itself spontaneously for a new problem allows us to realize
conventional computing constructs like logic gates and Voronoi decompositions,
as well as to reproduce two natural phenomena: heat diffusion and the mutation
of normal cells to cancer cells. This is a shift from the current static
computing paradigm of serial bit-processing to a regime in which a large number
of bits are processed in parallel in dynamically changing hardware.Comment: 25 pages, 6 figure
Cooperative Epistemic Multi-Agent Planning for Implicit Coordination
Epistemic planning can be used for decision making in multi-agent situations
with distributed knowledge and capabilities. Recently, Dynamic Epistemic Logic
(DEL) has been shown to provide a very natural and expressive framework for
epistemic planning. We extend the DEL-based epistemic planning framework to
include perspective shifts, allowing us to define new notions of sequential and
conditional planning with implicit coordination. With these, it is possible to
solve planning tasks with joint goals in a decentralized manner without the
agents having to negotiate about and commit to a joint policy at plan time.
First we define the central planning notions and sketch the implementation of a
planning system built on those notions. Afterwards we provide some case studies
in order to evaluate the planner empirically and to show that the concept is
useful for multi-agent systems in practice.Comment: In Proceedings M4M9 2017, arXiv:1703.0173
Quantitative Measures of Regret and Trust in Human-Robot Collaboration Systems
Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human\u27s rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human\u27s acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks
Modeling of Decision-making Processes to Ensure Sustainable Operation of Multiservice Communication Network
This paper shows the modeling of decision-making processes to ensure stable operation of multiservice communication networks (MCNs) using the mathematical apparatus of fuzzy logic models. A classification of the main factors affecting the stability of an MCN is given. The main factors affecting the structural stability of MCNs are external factors, internal factors, energy factors, and maintenance factors. A decision-making strategy (DM) was chosen. The main factors that affect the stability of the functioning of an MCN are characterized by heterogeneity. Therefore, the task of the DM to ensure stability of the functioning of the MCN was reduced to producing a sequential solution of the following interrelated tasks: identification of the MCN by a systematic analysis of the main factors affecting the stability of the MCN, ranking the states of the MCN, and definition of the decision-making criteria. The first point is implemented by setting up a complex model of the MCN based on integration of the principles of fuzzy set theory (FST). A promising method for choosing a rational alternative is the method of non-dominated alternatives (MNDA), based on the aggregation of fuzzy information to characterize the relationship between the alternatives according to certain criteria
Falsification-Based Robust Adversarial Reinforcement Learning
Reinforcement learning (RL) has achieved tremendous progress in solving
various sequential decision-making problems, e.g., control tasks in robotics.
However, RL methods often fail to generalize to safety-critical scenarios since
policies are overfitted to training environments. Previously, robust
adversarial reinforcement learning (RARL) was proposed to train an adversarial
network that applies disturbances to a system, which improves robustness in
test scenarios. A drawback of neural-network-based adversaries is that
integrating system requirements without handcrafting sophisticated reward
signals is difficult. Safety falsification methods allow one to find a set of
initial conditions as well as an input sequence, such that the system violates
a given property formulated in temporal logic. In this paper, we propose
falsification-based RARL (FRARL), the first generic framework for integrating
temporal-logic falsification in adversarial learning to improve policy
robustness. With falsification method, we do not need to construct an extra
reward function for the adversary. We evaluate our approach on a braking
assistance system and an adaptive cruise control system of autonomous vehicles.
Experiments show that policies trained with a falsification-based adversary
generalize better and show less violation of the safety specification in test
scenarios than the ones trained without an adversary or with an adversarial
network.Comment: 11 pages, 3 figure
Answer Set Programming for Non-Stationary Markov Decision Processes
Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains
Social Mental Shaping: Modelling the Impact of Sociality on Autonomous Agents' Mental States
This paper presents a framework that captures how the social nature of agents that are situated in a multi-agent environment impacts upon their individual mental states. Roles and relationships provide an abstraction upon which we develop the notion of social mental shaping. This allows us to extend the standard Belief-Desire-Intention model to account for how common social phenomena (e.g. cooperation, collaborative problem-solving and negotiation) can be integrated into a unified theoretical perspective that reflects a fully explicated model of the autonomous agent's mental state
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