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Contributions to Deep Learning Models - RiuNet
Contributions to Deep Learning Models - RiuNet

... Error rate (\%) on the MNIST test set for both clean and noisy images using different regularization schemes and varying the number of labeled samples used in the fine-tuning. . . . . . . . . . . . . . . . . . Effect of applying MSR to different layers of the network. Error rate (\%) on the MNIST te ...
querying description logic knowledge bases
querying description logic knowledge bases

... defined syntax and semantics. A Description Logic allows for the specification of concepts (also known as classes), individuals (also known as objects) that are instances of these concepts, and roles (also known as properties) that are interpreted as pairs of individuals that are related by the role ...
The Role of Subjectivity in Intelligent Systems Communication and
The Role of Subjectivity in Intelligent Systems Communication and

... the tools used by a subject to perform activities in a determinate context (Bernat 2011). Seen from the activity theory perspective Agency is in fact liable to change in response to new contextual developments, feature that is important in software agent communication activities. In this context, th ...
Financial Time Series Forecasting Using Improved Wavelet Neural
Financial Time Series Forecasting Using Improved Wavelet Neural

... Hybrid models do not always give better performance than individual models. For instance, [91] presents a Neural Net-PMRS hybrid model for forecasting exchange rates. The model uses a traditional multilayer neural network as predictor, but rather than using the last n observations for prediction, th ...
Intelligence by Design: Principles of Modularity and Coordination for
Intelligence by Design: Principles of Modularity and Coordination for

... All intelligence relies on search — for example, the search for an intelligent agent’s next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dis ...
Eliciting Single-Peaked Preferences Using Comparison Queries
Eliciting Single-Peaked Preferences Using Comparison Queries

... on its own). First, the elicitor provides the agent with a systematic way of assessing its preferences: all that the agent needs to do is answer simple queries. Second, and perhaps more importantly, once the elicitor has elicited the preferences of some agents, the elicitor will have some understand ...
Intelligence by Design - Department of Computer Science
Intelligence by Design - Department of Computer Science

... All intelligence relies on search — for example, the search for an intelligent agent’s next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dis ...
A Computational Model of Belief - Rochester CS
A Computational Model of Belief - Rochester CS

... are incompatible with a theory of how belief in one sentence (or proposition) is related to belief in another, only that they do not themselves include such a theory. Another model, the “possible worlds” model, has more structure. In this model, the set of sentences that a person believes can’t be a ...
The DL-Lite Family - Dipartimento di Informatica e Sistemistica
The DL-Lite Family - Dipartimento di Informatica e Sistemistica

... of constructs expressible in the knowledge base does not pose particular difficulties to TBox reasoning. Indeed, in spite of the simplicity of DL-Litecore TBoxes, the ability of taking TBox knowledge into account during the process of answering (unions of) conjunctive queries goes beyond the two-var ...
Dissection of Genetic Factors Modulating Fetal Growth in
Dissection of Genetic Factors Modulating Fetal Growth in

... one chromosome with a length of 1.2 M and an average marker density for the variance component analyses of 0.02927 M. LRT significance thresholds for a ¼ 0.05 and a ¼ 0.0001 were 10.5 and 24. The variance component approach used to model a random QTL effect (Perez-Enciso and Misztal 2004) further co ...
Aalborg Universitet
Aalborg Universitet

... the goal of arriving at the true diagnosis or conclusion of a given problem, but also a desire to do so as efficiently as possible. In layman’s terms, the faster (or cheaper) we arrive at the proper conclusion, the better. But decision theoretic troubleshooting goes further than that because it esta ...
Intelligent Distributed Agent Based Architecture
Intelligent Distributed Agent Based Architecture

... The new approach to coordination was inspired by social networks, as observed in higher mammalian societies. Two social relationships were explored, namely kinship and trust. Coordination is achieved through team selection. Using characteristics of social networks, such as learning and the ability t ...
Contrast-dependence of surround suppression in
Contrast-dependence of surround suppression in

... and 80% contrast); low contrast values were generally chosen to be those eliciting b 50% of the maximum response in the cell's contrastresponse function, but still eliciting a reliable response (at least 2 SD greater than the spontaneous firing rate; typically between 4% and 30%; the cell in Fig. 2c ...
Automated Negotiations Among Autonomous Agents
Automated Negotiations Among Autonomous Agents

... Dissertation Overview . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Gerenciamento Autônomo de Redes na Internet do
Gerenciamento Autônomo de Redes na Internet do

... network controlling mechanisms. Deploying such autonomous and rational entities in the network can improve its behavior in the presence of very dynamic and complex control scenarios. Unfortunately, building agent-based mechanisms for networks is not an easy task. The main difficulty is to create con ...
CS 561a: Introduction to Artificial Intelligence
CS 561a: Introduction to Artificial Intelligence

... • Agents are autonomous, that is, they act on behalf of the user • Agents contain some level of intelligence, from fixed rules to learning engines that allow them to adapt to changes in the environment • Agents don‘t only act reactively (被動地因應改變), but sometimes also proactively ( 主動地改變) ...
Artificial Intelligence
Artificial Intelligence

... agent simply by T ELLing it what it needs to know. The agent’s initial program, before it starts to receive percepts, is built by adding one by one the sentences that represent the designer’s knowledge of the environment. Designing the representation language to make it easy to express this knowledg ...
Negotiating Socially Optimal Allocations of Resources: An Overview
Negotiating Socially Optimal Allocations of Resources: An Overview

... are, in principle, also applicable in the distributed case, but research in this area has not yet reached the same level of maturity as for combinatorial auctions. An important argument against centralised approaches is that it may be difficult to find an agent that could assume the role of an “auc ...
Osmand Christian - XY Home
Osmand Christian - XY Home

... As much as agents help tackling interoperability problems, they also enable negotiation for services and resources. Agents are typically in heterogeneous systems with inherently distributed data, their own control and resources. Interactions become a core part of these agents, especially at run-time ...
pre-print - School of Computer Science, University of Birmingham.
pre-print - School of Computer Science, University of Birmingham.

... different varieties of simulative and non-simulative meta-reasoning carefully, as some varieties of non-simulative reasoning are not that different from some varieties of Simulative Reasoning. The other half of the chapter yokes Simulative Reasoning to a different and rather radical proposal about ...
teză de doctorat - AI-MAS
teză de doctorat - AI-MAS

... artificial agent behavior. Virtual characters acting in an intelligent manner based on their goals and desires as opposed to standing still, waiting around for the user and interacting based on pre-scripted dialog trees brings a great deal of depth to the experience. Evolving virtual worlds are by f ...
“left or right” Decision-making beyond
“left or right” Decision-making beyond

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cortical limbic system: a computational model. PhD thesis. htt
cortical limbic system: a computational model. PhD thesis. htt

... The striatum is a major input structure of the basal ganglia and is a target structure of dopaminergic neurons which originate from the mid brain. These dopaminergic neurons release dopamine which is known to exert modulatory influences on the striatal projections. Action selection and control are i ...
Intrusion detection using clustering
Intrusion detection using clustering

... In [12] modified K-mean clustering algorithm called KD clustering has been used for intrusion detection. In this clustering algorithm, Set S is initialized to null where S is the collection of clusters. For allocation of the data points, checked , if S is null then a new cluster is built and added i ...
Integrating Planning, Execution and Learning to Improve Plan
Integrating Planning, Execution and Learning to Improve Plan

... Figure 3. Execution algorithm for domains with dead-ends. the-shelf spirit of the architecture allows pela to acquire other useful execution information, such as the actions durations (Lanchas et al., 2007). 3.1. Learning rules about the actions performance For each action a ∈ A, pela learns a model ...
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Agent-based model in biology

Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system. The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior. Several of the characteristics of agent-based models important to biological studies include: Modular structure: The behavior of an agent-based model is defined by the rules of its agents. Existing agent rules can be modified or new agents can be added without having to modify the entire model. Emergent properties: Through the use of the individual agents that interact locally with rules of behavior, agent-based models result in a synergy that leads to a higher level whole with much more intricate behavior than those of each individual agent. Abstraction: Either by excluding non-essential details or when details are not available, agent-based models can be constructed in the absence of complete knowledge of the system under study. This allows the model to be as simple and verifiable as possible. Stochasticity: Biological systems exhibit behavior that appears to be random. The probability of a particular behavior can be determined for a system as a whole and then be translated into rules for the individual agents.Before the agent-based model can be developed, one must choose the appropriate software or modeling toolkit to be used. Madey and Nikolai provide an extensive list of toolkits in their paper ""Tools of the Trade: A Survey of Various Agent Based Modeling Platforms"". The paper seeks to provide users with a method of choosing a suitable toolkit by examining five characteristics across the spectrum of toolkits: the programming language required to create the model, the required operating system, availability of user support, the software license type, and the intended toolkit domain. Some of the more commonly used toolkits include Swarm, NetLogo, RePast, and Mason. Listed below are summaries of several articles describing agent-based models that have been employed in biological studies. The summaries will provide a description of the problem space, an overview of the agent-based model and the agents involved, and a brief discussion of the model results.
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