2,147 research outputs found
Asimovian Adaptive Agents
The goal of this research is to develop agents that are adaptive and
predictable and timely. At first blush, these three requirements seem
contradictory. For example, adaptation risks introducing undesirable side
effects, thereby making agents' behavior less predictable. Furthermore,
although formal verification can assist in ensuring behavioral predictability,
it is known to be time-consuming. Our solution to the challenge of satisfying
all three requirements is the following. Agents have finite-state automaton
plans, which are adapted online via evolutionary learning (perturbation)
operators. To ensure that critical behavioral constraints are always satisfied,
agents' plans are first formally verified. They are then reverified after every
adaptation. If reverification concludes that constraints are violated, the
plans are repaired. The main objective of this paper is to improve the
efficiency of reverification after learning, so that agents have a sufficiently
rapid response time. We present two solutions: positive results that certain
learning operators are a priori guaranteed to preserve useful classes of
behavioral assurance constraints (which implies that no reverification is
needed for these operators), and efficient incremental reverification
algorithms for those learning operators that have negative a priori results
Evolving Gene Regulatory Networks with Mobile DNA Mechanisms
This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1303.722
Learning Moore Machines from Input-Output Traces
The problem of learning automata from example traces (but no equivalence or
membership queries) is fundamental in automata learning theory and practice. In
this paper we study this problem for finite state machines with inputs and
outputs, and in particular for Moore machines. We develop three algorithms for
solving this problem: (1) the PTAP algorithm, which transforms a set of
input-output traces into an incomplete Moore machine and then completes the
machine with self-loops; (2) the PRPNI algorithm, which uses the well-known
RPNI algorithm for automata learning to learn a product of automata encoding a
Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore
machine using PTAP extended with state merging. We prove that MooreMI has the
fundamental identification in the limit property. We also compare the
algorithms experimentally in terms of the size of the learned machine and
several notions of accuracy, introduced in this paper. Finally, we compare with
OSTIA, an algorithm that learns a more general class of transducers, and find
that OSTIA generally does not learn a Moore machine, even when fed with a
characteristic sample
A Planning-based Approach for Music Composition
. Automatic music composition is a fascinating field within computational
creativity. While different Artificial Intelligence techniques have been used
for tackling this task, Planning – an approach for solving complex combinatorial
problems which can count on a large number of high-performance systems and
an expressive language for describing problems – has never been exploited.
In this paper, we propose two different techniques that rely on automated planning
for generating musical structures. The structures are then filled from the bottom
with “raw” musical materials, and turned into melodies. Music experts evaluated
the creative output of the system, acknowledging an overall human-enjoyable
trait of the melodies produced, which showed a solid hierarchical structure and a
strong musical directionality. The techniques proposed not only have high relevance
for the musical domain, but also suggest unexplored ways of using planning
for dealing with non-deterministic creative domains
Cloud Computing and Cloud Automata as A New Paradigm for Computation
Cloud computing addresses how to make right resources available to right computation to improve scaling, resiliency and efficiency of the computation. We argue that cloud computing indeed, is a new paradigm for computation with a higher order of artificial intelligence (AI), and put forward cloud automata as a new model for computation. A high-level AI requires infusing features that mimic human functioning into AI systems. One of the central features is that humans learn all the time and the learning is incremental. Consequently, for AI, we need to use computational models, which reflect incremental learning without stopping (sentience). These features are inherent in reflexive, inductive and limit Turing machines. To construct cloud automata, we use the mathematical theory of Oracles, which include Oracles of Turing machines as its special case. We develop a hierarchical approach based on Oracles with different ranks that includes Oracle AI as a special case. Discussing a named-set approach, we describe an implementation of a high-performance edge cloud using hierarchical name-oriented networking and Oracle AI-based orchestration. We demonstrate how cloud automata with a control overlay allows microservice network provisioning, monitoring and reconfiguration to address non-deterministic fluctuations affecting their behavior without interrupting the overall evolution of computation
Search-Based Evolution of XML Schemas
The use of schemas makes an XML-based application more reliable, since they contribute to avoid failures by defining the specific format for the data that the application manipulates. In practice, when an application evolves, new requirements for the data may be established, raising the need of schema evolution. In some cases the generation of a schema is necessary, if such schema does not exist. To reduce maintenance and reengineering costs, automatic evolution of schemas is very desirable. However, there are no algorithms to satisfactorily solve the problem. To help in this task, this paper introduces a search-based approach that explores the correspondence between schemas and context-free grammars. The approach is supported by a tool, named EXS. Our tool implements algorithms of grammatical inference based on LL(1) Parsing. If a grammar (that corresponds to a schema) is given and a new word (XML document) is provided, the EXS system infers the new grammar that: i) continues to generate the same words as before and ii) generates the new word, by modifying the original grammar. If no initial grammar is available, EXS is also capable of generating a grammar from scratch from a set of samples
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
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