1,093 research outputs found
Generating Levels That Teach Mechanics
The automatic generation of game tutorials is a challenging AI problem. While
it is possible to generate annotations and instructions that explain to the
player how the game is played, this paper focuses on generating a gameplay
experience that introduces the player to a game mechanic. It evolves small
levels for the Mario AI Framework that can only be beaten by an agent that
knows how to perform specific actions in the game. It uses variations of a
perfect A* agent that are limited in various ways, such as not being able to
jump high or see enemies, to test how failing to do certain actions can stop
the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International
Workshop on Procedural Content Generation (PCG2018
The meaning of life in a developing universe
The evolution of life on Earth has produced an organism that is beginning to model and understand its own evolution and the possible future evolution of life in the universe. These models and associated evidence show that evolution on Earth has a trajectory. The scale over which living processes are organized cooperatively has increased progressively, as has its evolvability. Recent theoretical advances raise the possibility that this trajectory is itself part of a wider developmental process. According to these theories, the developmental process has been shaped by a larger evolutionary process that involves the reproduction of universes. This evolutionary process has tuned the key parameters of the universe to increase the likelihood that life will emerge and develop to produce outcomes that are successful in the larger process (e.g. a key outcome may be to produce life and intelligence that intentionally reproduces the universe and tunes the parameters of ‘offspring’ universes). Theory suggests that when life emerges on a planet, it moves along this trajectory of its own accord. However, at a particular point evolution will continue to advance only if organisms emerge that decide to advance the evolutionary process intentionally. The organisms must be prepared to make this commitment even though the ultimate nature and destination of the process is uncertain, and may forever remain unknown. Organisms that complete this transition to intentional evolution will drive the further development of life and intelligence in the universe. Humanity’s increasing understanding of the evolution of life in the universe is rapidly bringing it to the threshold of this major evolutionary transition
Architecting system of systems: artificial life analysis of financial market behavior
This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Diagnosis of an EPS module
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova
de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e ComputadoresThis thesis addresses and contextualizes the problem of diagnostic of an Evolvable
Production System (EPS). An EPS is a complex and lively entity composed of intelligent modules that interact through bio-inspired mechanisms, to ensure high system availability and seamless reconfiguration.
The actual economic situation together with the increasing demand of high quality and low
priced customized products imposed a shift in the production policies of enterprises. Shop floors have to become more agile and flexible to accommodate the new production paradigms. Rather than selling products enterprises are establishing a trend of offering services to explore business
opportunities.
The new production paradigms, potentiated by the advances in Information Technologies
(IT), especially in web related standards and technologies as well as the progressive acceptance of the multi-agent systems (MAS) concept and related technologies, envision collections of modules whose individual and collective function adapts and evolves ensuring the fitness and adequacy of the shop
floor in tackling profitable but volatile business opportunities. Despite the richness of the interactions and the effort set in modelling them, their potential to favour fault propagation and interference, in
these complex environments, has been ignored from a diagnostic point of view.
With the increase of distributed and autonomous components that interact in the execution of processes current diagnostic approaches will soon be insufficient. While current system dynamics are complex and to a certain extent unpredictable the adoption of the next generation of approaches and technologies comes at the cost of a yet increased complexity.Whereas most of the research in such distributed industrial systems is focused in the study and establishment of control structures, the problem of diagnosis has been left relatively unattended.
There are however significant open challenges in the diagnosis of such modular systems including:
understanding fault propagation and ensuring scalability and co-evolution.
This work provides an implementation of a state-of-the-art agent-based interaction-oriented architecture compliant with the EPS paradigm that supports the introduction of a new developed diagnostic algorithm that has the ability to cope with the modern manufacturing paradigm challenges and to provide diagnostic analysis that explores the network dimension of multi-agent systems
Dpws middleware to support agent-based manufacturing control and simulation
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresIn present manufacturing systems, the current challenge is the development of highly reconfigurable, truly distributed solutions. The tendency is to build manufacturing systems with autonomous, intelligent and distributed components that will support reconfiguration and
adaptability. The most promising paradigms for the implementation of such systems are
multi-agents and service oriented architectures (SOA), mainly over the DPWS (Device Profile for Web Services) implementation which was aimed at devices.
An important limitation of most current multi-agent systems is that the management
system is not totally distributed. Failure in the agent responsible for the registry can
overthrow the entire system. DPWS does not have this limitation, since the management
system is totally distributed. However, DPWS does not support agent autonomy notions as
efficiently.
The possibility of creating a truly distributed multi-agent system by linking both approaches led to this thesis. A Middleware layer was developed that enables agents to benefit from DPWS functionalities in order to reach the proposed goal. This middleware layer joins agents, databases, hardware, simulators, human interface applications such as production system management, error correction and maintenance, etc. To prove this concept a 3D model of an agent controlled manufacturing system with transporters augmented with DPWS
communication interfaces was developed
Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League
We describe Gliders2d, a base code release for Gliders, a soccer simulation
team which won the RoboCup Soccer 2D Simulation League in 2016. We trace six
evolutionary steps, each of which is encapsulated in a sequential change of the
released code, from v1.1 to v1.6, starting from agent2d-3.1.1 (set as the
baseline v1.0). These changes improve performance by adjusting the agents'
stamina management, their pressing behaviour and the action-selection
mechanism, as well as their positional choice in both attack and defense, and
enabling riskier passes. The resultant behaviour, which is sufficiently generic
to be applicable to physical robot teams, increases the players' mobility and
achieves a better control of the field. The last presented version,
Gliders2d-v1.6, approaches the strength of Gliders2013, and outperforms
agent2d-3.1.1 by four goals per game on average. The sequential improvements
demonstrate how the methodology of human-based evolutionary computation can
markedly boost the overall performance with even a small number of controlled
steps.Comment: 12 pages, 1 figure, Gliders2d code releas
Skill-based reconfiguration of industrial mobile robots
Caused by a rising mass customisation and the high variety of equipment versions, the
exibility of manufacturing systems in car productions has to be increased. In addition to
a
exible handling of production load changes or hardware breakdowns that are established
research areas in literature, this thesis presents a skill-based recon guration mechanism
for industrial mobile robots to enhance functional recon gurability.
The proposed holonic multi-agent system is able to react to functional process changes
while missing functionalities are created by self-organisation. Applied to a mobile commissioning
system that is provided by AUDI AG, the suggested mechanism is validated
in a real-world environment including the on-line veri cation of the recon gured robot
functionality in a Validity Check.
The present thesis includes an original contribution in three aspects: First, a recon -
guration mechanism is presented that reacts in a self-organised way to functional process
changes. The application layer of a hardware system converts a semantic description into
functional requirements for a new robot skill. The result of this mechanism is the on-line
integration of a new functionality into the running process.
Second, the proposed system allows maintaining the productivity of the running process
and
exibly changing the robot hardware through provision of a hardware-abstraction
layer. An encapsulated Recon guration Holon dynamically includes the actual con guration
each time a recon guration is started. This allows reacting to changed environment
settings. As the resulting agent that contains the new functionality, is identical in shape
and behaviour to the existing skills, its integration into the running process is conducted
without a considerable loss of productivity.
Third, the suggested mechanism is composed of a novel agent design that allows implementing
self-organisation during the encapsulated recon guration and dependability
for standard process executions. The selective assignment of behaviour-based and cognitive
agents is the basis for the
exibility and e ectiveness of the proposed recon guration
mechanism
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
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