Search results for "Decision process"

F-contractions of Hardy–Rogers-type and application to multistage decision

2016

We prove fixed point theorems for F-contractions of Hardy–Rogers type involving self-mappings defined on metric spaces and ordered metric spaces. An example and an application to multistage decision processes are given to show the usability of the obtained theorems.

Cross inhibition improves activity selection when switching incurs time costs

2015

Abstract We consider a behavioural model of an animal choosing between two activities, based on positive feedback, and examine the effect of introducing cross inhibition between the motivations for the two activities. While cross-inhibition has previously been included in models of decision making, the question of what benefit it may provide to an animal’s activity selection behaviour has not previously been studied. In neuroscience and in collective behaviour cross-inhibition, and other equivalent means of coupling evidence-accumulating pathways, have been shown to approximate statistically-optimal decision-making and to adaptively break deadlock, thereby improving decision performance. Sw…

MDP-Based Resource Allocation Scheme Towards a Vehicular Fog Computing with Energy Constraints

2018

As mobile applications deliver increasingly complex functionalities, the demands for even more intensive computation would quickly transcend energy capability of mobile devices. On one hand and in an attempt to address such issues, fog computing paradigm is introduced to mitigate the limited energy and computation resources available within constrained mobile devices, by moving computation resources closer to their users at the edge of the access network. On another hand, most of electric vehicles (EVs), with increasing computation, storage and energy capabilities, spend more than 90% of time on parking lots. In this paper, we conceive the basic idea of using the underutilized computation r…

A fully automated system for the evaluation of masseter silent periods.

1997

Exteroceptive suppression of masseter muscle activity, 'masseter inhibitory reflex', comprises one or 2 silent periods (SP1 and SP2) interrupting the voluntary activation. The main problem when evaluating exteroceptive suppression is the lack of an objective and precise measure for the onset and end of the silent period which so far has not been overcome by various automated systems. We describe a new fully automated system for determining the onset and end of the masseter silent period. The decision approach is essentially based upon deterministic properties of median filters which are used to partition the local variances of the EMG traces into constant segments and edges between them. Th…

Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing

2020

In mission-critical, real-world environments, there is typically a low threshold for failure, which makes interaction with learning algorithms particularly challenging. Here, current state-of-the-art reinforcement learning algorithms struggle to learn optimal control policies safely. Loss of control follows, which could result in equipment breakages and even personal injuries.

Increasing sample efficiency in deep reinforcement learning using generative environment modelling

2020

CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning

2020

Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as \(\epsilon \)-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-opti…

Some Effects of Individual Learning on the Evolution of Sensors

2001

In this paper, we present an abstract model of sensor evolution, where sensor development is only determined by artificial evolution and the adaptation of agent reactions is accomplished by individual learning. With the environment cast into a MDP framework, sensors can be conceived as a map from environmental states to agent observations and Reinforcement Learning algorithms can be utilised. On the basis of a simple gridworld scenario, we present some results of the interaction between individual learning and evolution of sensors.

Shakedown Analysis by Elastic Simulation

2000

Shakedown analysis of elastic plastic structures is widely credited as a valuable analytical/numerical tool for design purposes. For complex structures and loading conditions, e. g. for fast breeder nuclear reactor plants, full inelastic analysis is rarely performed, practically never within the early stages of the design advancement and the inherent decision process. The essential information therein needed can in fact be obtained, at moderate computational costs, by application of the shakedown methods and rules, at least within some limits related to the present developments of shakedown theory and its applicability to practical engineering problems, see e. g. Ponter et al. (1990), Carte…

Continuous energy-efficient monitoring model for mobile ad hoc networks

2021

The monitoring of mobile ad hoc networks is an observation task that consists of analysing the operational status of these networks while evaluating their functionalities. In order to allow the whole network and applications to work properly, the monitoring task has become of considerable importance. It must be carried out in real-time by performing measurements, logs, configurations, etc. However, achieving continuous energy-efficient monitoring in mobile wireless networks is very challenging considering the environment features as well as the unpredictable behavior of the participating nodes. This paper outlines the challenges of continuous energy-efficient monitoring over mobile ad hoc n…