74,257 research outputs found
A Web Smart Space Framework for Intelligent Search Engines
A web smart space is an intelligent environment which has additional capability of searching the information smartly and efficiently. New advancements like dynamic web contents generation has increased the size of web repositories. Among so many modern software analysis requirements, one is to search information from the given repository. But useful information extraction is a troublesome hitch due to the multi-lingual; base of the web data collection. The issue of semantic based information searching has become a standoff due to the inconsistencies and variations in the characteristics of the data. In the accomplished research, a web smart space framework has been proposed which introduces front end processing for a search engine to make the information retrieval process more intelligent and accurate. In orthodox searching anatomies, searching is performed only by using pattern matching technique and consequently a large number of irrelevant results are generated. The projected framework has insightful ability to improve this drawback and returns efficient outcomes. Designed framework gets text input from the user in the form complete question, understands the input and generates the meanings. Search engine searches on the basis of the information provided
Reinforcement Learning: A Survey
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 file
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Intrinsically motivated spontaneous exploration is a key enabler of
autonomous lifelong learning in human children. It enables the discovery and
acquisition of large repertoires of skills through self-generation,
self-selection, self-ordering and self-experimentation of learning goals. We
present an algorithmic approach called Intrinsically Motivated Goal Exploration
Processes (IMGEP) to enable similar properties of autonomous or self-supervised
learning in machines. The IMGEP algorithmic architecture relies on several
principles: 1) self-generation of goals, generalized as fitness functions; 2)
selection of goals based on intrinsic rewards; 3) exploration with incremental
goal-parameterized policy search and exploitation of the gathered data with a
batch learning algorithm; 4) systematic reuse of information acquired when
targeting a goal for improving towards other goals. We present a particularly
efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a
population-based policy and an object-centered modularity in goals and
mutations. We provide several implementations of this architecture and
demonstrate their ability to automatically generate a learning curriculum
within several experimental setups including a real humanoid robot that can
explore multiple spaces of goals with several hundred continuous dimensions.
While no particular target goal is provided to the system, this curriculum
allows the discovery of skills that act as stepping stone for learning more
complex skills, e.g. nested tool use. We show that learning diverse spaces of
goals with intrinsic motivations is more efficient for learning complex skills
than only trying to directly learn these complex skills
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
The focus of this paper is on solving multi-robot planning problems in
continuous spaces with partial observability. Decentralized partially
observable Markov decision processes (Dec-POMDPs) are general models for
multi-robot coordination problems, but representing and solving Dec-POMDPs is
often intractable for large problems. To allow for a high-level representation
that is natural for multi-robot problems and scalable to large discrete and
continuous problems, this paper extends the Dec-POMDP model to the
decentralized partially observable semi-Markov decision process (Dec-POSMDP).
The Dec-POSMDP formulation allows asynchronous decision-making by the robots,
which is crucial in multi-robot domains. We also present an algorithm for
solving this Dec-POSMDP which is much more scalable than previous methods since
it can incorporate closed-loop belief space macro-actions in planning. These
macro-actions are automatically constructed to produce robust solutions. The
proposed method's performance is evaluated on a complex multi-robot package
delivery problem under uncertainty, showing that our approach can naturally
represent multi-robot problems and provide high-quality solutions for
large-scale problems
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