189 research outputs found
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Surrogate-assistance approaches have long been used in computationally
expensive domains to improve the data-efficiency of optimization algorithms.
Neuroevolution, however, has so far resisted the application of these
techniques because it requires the surrogate model to make fitness predictions
based on variable topologies, instead of a vector of parameters. Our main
insight is that we can sidestep this problem by using kernel-based surrogate
models, which require only the definition of a distance measure between
individuals. Our second insight is that the well-established Neuroevolution of
Augmenting Topologies (NEAT) algorithm provides a computationally efficient
distance measure between dissimilar networks in the form of "compatibility
distance", initially designed to maintain topological diversity. Combining
these two ideas, we introduce a surrogate-assisted neuroevolution algorithm
that combines NEAT and a surrogate model built using a compatibility distance
kernel. We demonstrate the data-efficiency of this new algorithm on the low
dimensional cart-pole swing-up problem, as well as the higher dimensional
half-cheetah running task. In both tasks the surrogate-assisted variant
achieves the same or better results with several times fewer function
evaluations as the original NEAT.Comment: In GECCO 201
Methodological Investigations in Agent-Based Modelling: With Applications for the Social Sciences
This open access book examines the methodological complications of using complexity science concepts within the social science domain. The opening chapters take the reader on a tour through the development of simulation methodologies in the fields of artificial life and population biology, then demonstrates the growing popularity and relevance of these methods in the social sciences. Following an in-depth analysis of the potential impact of these methods on social science and social theory, the text provides substantive examples of the application of agent-based models in the field of demography. This work offers a unique combination of applied simulation work and substantive, in-depth philosophical analysis, and as such has potential appeal for specialist social scientists, complex systems scientists, and philosophers of science interested in the methodology of simulation and the practice of interdisciplinary computing research.
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
Computational Theories of Curiosity-Driven Learning
What are the functions of curiosity? What are the mechanisms of
curiosity-driven learning? We approach these questions about the living using
concepts and tools from machine learning and developmental robotics. We argue
that curiosity-driven learning enables organisms to make discoveries to solve
complex problems with rare or deceptive rewards. By fostering exploration and
discovery of a diversity of behavioural skills, and ignoring these rewards,
curiosity can be efficient to bootstrap learning when there is no information,
or deceptive information, about local improvement towards these problems. We
also explain the key role of curiosity for efficient learning of world models.
We review both normative and heuristic computational frameworks used to
understand the mechanisms of curiosity in humans, conceptualizing the child as
a sense-making organism. These frameworks enable us to discuss the
bi-directional causal links between curiosity and learning, and to provide new
hypotheses about the fundamental role of curiosity in self-organizing
developmental structures through curriculum learning. We present various
developmental robotics experiments that study these mechanisms in action, both
supporting these hypotheses to understand better curiosity in humans and
opening new research avenues in machine learning and artificial intelligence.
Finally, we discuss challenges for the design of experimental paradigms for
studying curiosity in psychology and cognitive neuroscience.
Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions,
world model, rewards, free-energy principle, learning progress, machine
learning, AI, developmental robotics, development, curriculum learning,
self-organization.Comment: To appear in "The New Science of Curiosity", ed. G. Gordon, Nova
Science Publisher
Evolutionary Mechanism Design
The advent of large-scale distributed systems poses unique engineering challenges. In open
systems such as the internet it is not possible to prescribe the behaviour of all of the
components of the system in advance. Rather, we attempt to design infrastructure, such as
network protocols, in such a way that the overall system is robust despite the fact that
numerous arbitrary, non-certified, third-party components can connect to our system.
Economists have long understood this issue, since it is analogous to the design of the rules
governing auctions and other marketplaces, in which we attempt to achieve sociallydesirable
outcomes despite the impossibility of prescribing the exact behaviour of the
market participants, who may attempt to subvert the market for their own personal gain.
This field is known as 'mechanism design': the science of designing rules of a game to
achieve a specific outcome, even though each participant may be self-interested. Although
it originated in economics, mechanism design has become an important foundation of
multi-agent systems (MAS) research. In many scenarios mechanism design and auction
theory yield clear-cut results; however, there are many situations in which the underlying
assumptions of the theory are violated due to the messiness of the real-world. In this thesis
I introduce an evolutionary methodology for mechanism design, which is able to incorporate
arbitrary design objectives and domain assumptions, and I validate the methodology using
empirical techniques
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