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Distributed Classifier System for Smart Home’s Machine Learning
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This general paper aims at presenting the Smart Home concept. In this paper, we will detail a) the Smart Home concept b) the various networks infrastructures specific to the habitat c) our concepts to model the habitat and to provide the most adapted services to the inhabitants. Contrary to the other projects, we direct our work towards a sensors approach and an ontology modelling of the Smart Home. Our work has the originality to take into account the real heterogeneity of information present in a habitat and use a service oriented approach (SOA). We can say that our paper is a good overview to present what is a Smart Home and which are the necessary hardware and software components to make a Smart Home
Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
Abstract-This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets.
We asked ‘What is a Learning Classifier System’ to some of the best-known researchers in the field. These are their answers.
A learning classifier system has been found to be a feasible method for multi-agent systems. It provides the architecture necessary for communication and cooperation among agents. Although the learning classifier system has been proven capable of adapting to a changing environment, there has not been much research focusing on its performance when the number of agents changes. In this chapter, we propose an on-the-fly learning framework for cases where the number of agents changes continuously throughout the simulation; it that does not require restarting the learning process from the beginning.
Conceptions of the Home.- Inside the Smart Home: Ideas, Possibilities and Methods.- Conceptions of the Home.- Smart Homes: Past, Present and Future.- Households as Morally Ordered Communities: Explorations in the Dynamics of Domestic Life.- Time as a Rare Commodity in Home Life.- Emotional Context and "Significancies" of Media.- Designing for the Home.- Paper-mail in the Home of the 21st Century.- Switching On to Switch Off.- The Social Context of Home Computing.- Design with Care: Technology, Disability and the Home.- The Home of the Future.- Towards the Unremarkable Computer: Making Technology at Home in Domestic Routine.- Daily Routines and Means of Communication in a Smart Home.- Living Inside a Smart Home: A Case Study.- Smart Home, Dumb Suppliers? The Future of Smart Homes Markets.
A learning classifier system (LCS) is a model of an intelligent agent interacting with an environment. Many complex yet powerful LCS models exist today. However, they are designed with a single agent approach in mind. LCS applications in multi-agent environment have been problematic. Their complexity limits the agents' cooperation and coordination abilities. This study proposes a simple LCS model for a multi-agent system that allows agents to cooperate and coordinate their actions. New learning methods inspired by organizational learning theories are introduced, giving the agents a capability to recognize useful knowledge. It not only prevents the knowledge from being "forgotten" due to evolutionary process, but also transfers it into less experienced agents. Results show that, with these implementations, the agents manage to coordinate actions better than typical LCS model.
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning. Comment: See http://www.jair.org/ for any accompanying files
Examines the correlation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. Also discusses the difficulty in balancing resource management between gaining new information about alternatives to improve future returns (i.e., exploration) and using information currently available to improve present returns (i.e., exploitation). Two models which evaluate the formation and use of knowledge in organizations are developed. The first is a model of mutual learning in a closed system having fixed organizational membership and stability. The second is a model which considers the ways in which competitive advantage is affected by knowledge accumulation. The analysis indicates that the choice to rapidly develop exploitation over exploration might be effective in the short term, but is potentially detrimental to the firm in the long term. (SFL)
Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/46947/1/10994_2005_Article_422926.pdf
Inside the smart home, ISBN 1852338547
- R Harper
What is a learning classifier system?
- J H Holland
- JH Holland