Search results for "artificial intelligence"
showing 10 items of 6122 documents
A Neuro-Ethological Approach for the TSP: Changing Metaphors in Connectionist Models.
1994
Biological systems often offer solutions to difficult problems which are not only original but also efficient. Connectionist models have been inspired by neural systems and successfully applied to the formulation of algorithms for solving complex problems such as the travelling salesman problem. In this paper we extend the connectionist metaphor to include an ethological account of how problems similar to the travelling salesman problem are solved by real living systems. A model is presented in which a population of neural networks with simple sensory-motor systems evolve genetically in simulated environments which represent the problem instances to be solved. Preliminary results are discu…
The Use of Latent Semantic Analysis in the Positive Psychology: A Comparison with Twitter Posts
2017
In the last decade, the positive psychology and specifically the 'Positive Youth Development' (PYD) give efforts to positive aspect and strength that performance as protective factors of adjustment problems and psycho-social well-being, such as courage. To better understand the definition of courage in Italian context, 1199 participants were involved in the present study and we asked them to answer to the following question "Courage is...". The participant's definitions of courage were analyzed with the Latent Semantic Analysis (LSA), in order to study the "fundamental concepts" arising from the population. An analogous comparison with Twitter posts has been also carried out.
An adaption mechanism for the error threshold of XCSF
2020
Learning Classifier System (LCS) is a class of rule-based learning algorithms, which combine reinforcement learning (RL) and genetic algorithm (GA) techniques to evolve a population of classifiers. The most prominent example is XCS, for which many variants have been proposed in the past, including XCSF for function approximation. Although XCSF is a promising candidate for supporting autonomy in computing systems, it still must undergo parameter optimization prior to deployment. However, in case the later deployment environment is unknown, a-priori parameter optimization is not possible, raising the need for XCSF to automatically determine suitable parameter values at run-time. One of the mo…
Towards a Simulator of Integrated Long-Term Care Systems for Elderly People
2014
In this paper, we propose a simulator for integrated long-term care systems using as a starting point a holistic model of care systems for people that need long term care, the Sustainable Socio-Health Model (SSHM). The implementation of the simulator on the Jason multi-agent platform allows the tool to include the human interactions, preferences, and social abilities that take place between elderly people and the staff of healthcare systems (doctors, social workers and nurses). In addition, the use of this multi-agent platform provides the required scalability for simulating population sizes of different orders of magnitude. The paper shows the model to be implemented in the simulator, the…
A hybrid evolution strategy for the open vehicle routing problem
2010
This paper presents a hybrid evolution strategy (ES) for solving the open vehicle routing problem (OVRP), which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of non-depot returning capacitated vehicles. The objective is to minimize the fleet size and the distance traveled. The proposed solution method manipulates a population of @m individuals using a (@m+@l)-ES; at each generation, a new intermediate population of @l offspring is produced via mutation, using arcs extracted from parent individuals. The selection and combination of arcs is dictated by a vector of strategy parameters. A multi-parent recombination …
Set Membership (In) Validation of nonlinear positive models for biological systems
2006
The complexity of biology needs quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, Nonlinear Positive models with constraints for biological systems are validated/invalidated in a worst-case deterministic setting. These models are usefull for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. The conditional central estimate and the Uncertainty Intervals are determined in order to validate/invalidate the model. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments.
Memetic Compact Differential Evolution for Cartesian Robot Control
2010
This article deals with optimization problems to be solved in the absence of a full power computer device. The goal is to solve a complex optimization problem by using a control card related to portable devices, e.g. for the control of commercial robots. In order to handle this class of optimization problems, a novel Memetic Computing approach is presented. The proposed algorithm employs a Differential Evolution framework which instead of processing an actual population of candidate solutions, makes use of a statistical representation of the population which evolves over time. In addition, the framework uses a stochastic local search algorithm which attempts to enhance the performance of th…
Smartphone data analysis for human activity recognition
2017
In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the userâs context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a …
An analysis of the bias of variation operators of estimation of distribution programming
2018
Estimation of distribution programming (EDP) replaces standard GP variation operators with sampling from a learned probability model. To ensure a minimum amount of variation in a population, EDP adds random noise to the probabilities of random variables. This paper studies the bias of EDP's variation operator by performing random walks. The results indicate that the complexity of the EDP model is high since the model is overfitting the parent solutions when no additional noise is being used. Adding only a low amount of noise leads to a strong bias towards small trees. The bias gets stronger with an increased amount of noise. Our findings do not support the hypothesis that sampling drift is …
A distributed visualization system for crowd simulations1
2011
The visualization system of large-scale crowd simulations should scale up with both the number of visuals views of the virtual world and the number of agents displayed in each visual. Otherwise, we could have large scale crowd simulations where only a small percentage of the population is displayed. Several approaches have been proposed in order to efficiently render crowds of animated characters. However, these approaches either render crowds animated with simple behaviors or they can only support a few hundreds of user-driven entities. In this paper, we propose a distributed visualization system for large crowds of autonomous agents that allows the visualization of crowds animated with co…