Search results for "Theoretical Computer Science"
showing 10 items of 1151 documents
Probabilistic inferences from conjoined to iterated conditionals
2017
Abstract There is wide support in logic, philosophy, and psychology for the hypothesis that the probability of the indicative conditional of natural language, P ( if A then B ) , is the conditional probability of B given A, P ( B | A ) . We identify a conditional which is such that P ( if A then B ) = P ( B | A ) with de Finetti's conditional event, B | A . An objection to making this identification in the past was that it appeared unclear how to form compounds and iterations of conditional events. In this paper, we illustrate how to overcome this objection with a probabilistic analysis, based on coherence, of these compounds and iterations. We interpret the compounds and iterations as cond…
Delay-dependent exponential stabilization of positive 2D switched state-delayed systems in the Roesser model
2014
This paper deals with the controller synthesis for a class of positive two-dimensional (2D) switched delay systems described by the Roesser model. This kind of systems has the property that the states take nonnegative values whenever the initial boundaries are nonnegative, some delay-dependent sufficient conditions for the exponential stability of positive 2D switched systems with state delays are given. Furthermore, the design of positive state feedback controller under which the resulting closed-loop system meets the requirements of positivity and exponential stability is presented in terms of linear matrix inequalities (LMIs). An example is included to illustrate the effectiveness of the…
Performance and energy optimisation in CPUs through fuzzy knowledge representation
2019
Abstract This paper presents an automatic design space exploration using processor design knowledge for the multi-objective optimisation of a superscalar microarchitecture enhanced with selective load value prediction (SLVP). We introduced new important SLVP parameters and determined their influence regarding performance, energy consumption, and thermal dissipation. We significantly enlarged initial processor design knowledge expressed through fuzzy rules and we analysed its role in the process of automatic design space exploration. The proposed fuzzy rules improve the diversity and quality of solutions, and the convergence speed of the design space exploration process. Experiments show tha…
A formal model based on Game Theory for the analysis of cooperation in distributed service discovery
2016
New systems can be designed, developed, and managed as societies of agents that interact with each other by offering and providing services. These systems can be viewed as complex networks where nodes are bounded rational agents. In order to deal with complex goals, they require cooperation of the other agents to be able to locate the required services. The aim of this paper is formally and empirically analyze under which circumstances cooperation emerges in decentralized search of services. We propose a repeated game model that formalizes the interactions among agents in a search process where agents are free to choose between cooperate or not in the process. Agents make decisions based on…
Towards safe reinforcement-learning in industrial grid-warehousing
2020
Abstract Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment tra…
An ant colony optimization-based fuzzy predictive control approach for nonlinear processes
2015
In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with p…
Network-based H∞ output feedback control for uncertain stochastic systems
2013
This paper investigates the problem of network-based Floc, output feedback control for a class of stochastic nonlinear systems. A novel model is proposed to describe the systems taken into two sides of communication channels in the network environment, which is more general than one side of communication channel. The design procedure of observer-based controller is presented, which, guarantees the asymptotic stability in the mean square of the resulting closed-loop system with an H-infinity performance. Finally, a crane example is utilized to show the effectiveness and potential of the developed techniques. Refereed/Peer-reviewed
An efficient method for clustered multi-metric learning
2019
Abstract Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one.…
Adaptive memory programming for the dynamic bipartite drawing problem
2020
Abstract The bipartite drawing problem is a well-known NP-hard combinatorial optimization problem with numerous applications. The aim is to minimize the number of edge crossings in a two-layer graph, in which the edges are drawn as straight lines. We consider the dynamic variant of this problem, called the dynamic bipartite drawing problem (DBDP), which consists of adding (resp. or removing) vertices and edges to (resp. or from) a given bipartite drawing, thereby obtaining a new drawing with a layout similar to that of the original drawing. To solve this problem, we propose a tabu search method that incorporates adaptive memory to search the solution space efficiently. In this study, we com…
Domain-specific knowledge representation and inference engine for an intelligent tutoring system
2013
One of the most challenging steps in learning algebra is the translation of word problems into symbolic notation. This paper describes an Intelligent Tutoring System (ITS) that focuses on this stage of the problem solving process. On the one hand, a domain specific inference engine and a knowledge representation mechanism are proposed. These are based on a description language based on hypergraphs, and the idea of using conceptual schemes to represent the student's knowledge. As a result, the system is able to simultaneously: (a) represent all potential algebraic solutions to a given word problem; (b) keep track of the student's actions; (c) univocally determine the current state of the res…