940,643 research outputs found
Simple threshold rules solve explore/exploit tradeāoffs in a resource accumulation search task
How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation tradeāoffs using a novel card selection task that captures the common situation of searching among multiple resources (e.g., jobs) that can be exploited without depleting. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants' behavior on this task with random, threshold, and sampling strategies, and find that a linear decreasing threshold rule best fits participants' results. Further evidence that participants use decreasing thresholdābased strategies comes from reaction time differences between exploration and exploitation; however, participants themselves report nonādecreasing thresholds. Decreasing threshold strategies that āfrontāloadā exploration and switch quickly to exploitation are particularly effective in resource accumulation tasks, in contrast to optimal stopping problems like the Secretary Problem requiring longer exploration
Generic Strategies for Chemical Space Exploration
Computational approaches to exploring "chemical universes", i.e., very large
sets, potentially infinite sets of compounds that can be constructed by a
prescribed collection of reaction mechanisms, in practice suffer from a
combinatorial explosion. It quickly becomes impossible to test, for all pairs
of compounds in a rapidly growing network, whether they can react with each
other. More sophisticated and efficient strategies are therefore required to
construct very large chemical reaction networks.
Undirected labeled graphs and graph rewriting are natural models of chemical
compounds and chemical reactions. Borrowing the idea of partial evaluation from
functional programming, we introduce partial applications of rewrite rules.
Binding substrate to rules increases the number of rules but drastically prunes
the substrate sets to which it might match, resulting in dramatically reduced
resource requirements. At the same time, exploration strategies can be guided,
e.g. based on restrictions on the product molecules to avoid the explicit
enumeration of very unlikely compounds. To this end we introduce here a generic
framework for the specification of exploration strategies in graph-rewriting
systems. Using key examples of complex chemical networks from sugar chemistry
and the realm of metabolic networks we demonstrate the feasibility of a
high-level strategy framework.
The ideas presented here can not only be used for a strategy-based chemical
space exploration that has close correspondence of experimental results, but
are much more general. In particular, the framework can be used to emulate
higher-level transformation models such as illustrated in a small puzzle game
Self-adaptive exploration in evolutionary search
We address a primary question of computational as well as biological research
on evolution: How can an exploration strategy adapt in such a way as to exploit
the information gained about the problem at hand? We first introduce an
integrated formalism of evolutionary search which provides a unified view on
different specific approaches. On this basis we discuss the implications of
indirect modeling (via a ``genotype-phenotype mapping'') on the exploration
strategy. Notions such as modularity, pleiotropy and functional phenotypic
complex are discussed as implications. Then, rigorously reflecting the notion
of self-adaptability, we introduce a new definition that captures
self-adaptability of exploration: different genotypes that map to the same
phenotype may represent (also topologically) different exploration strategies;
self-adaptability requires a variation of exploration strategies along such a
``neutral space''. By this definition, the concept of neutrality becomes a
central concern of this paper. Finally, we present examples of these concepts:
For a specific grammar-type encoding, we observe a large variability of
exploration strategies for a fixed phenotype, and a self-adaptive drift towards
short representations with highly structured exploration strategy that matches
the ``problem's structure''.Comment: 24 pages, 5 figure
Keynote address
Approved and projected probe mission strategies for the outer planet exploration programs are briefly outlined
Lunar Resource Assessment: Strategies for Surface Exploration
Use of the indigenous resources of space to support long-term human presence is an essential element of the settlement of other planetary bodies. We are in a very early stage of understanding exactly how and under what circumstances space resources will become important. The materials and processes to recover them that we now think are critical may not ultimately be the raison d'etre for a resource utilization program. However, the need for strategic thinking proceeds in parallel with efforts to implement such plans and it is not too soon to begin thinking how we could and should use the abundant resources of materials and energy available from the Moon. The following commodities from the Moon are discussed: (1) bulk regolith, for shielding and construction on the lunar surface (ultimately for export to human-tended stations in Earth-Moon space), and (2) oxygen and hydrogen, for propellant and life support
Decision Making for Rapid Information Acquisition in the Reconnaissance of Random Fields
Research into several aspects of robot-enabled reconnaissance of random
fields is reported. The work has two major components: the underlying theory of
information acquisition in the exploration of unknown fields and the results of
experiments on how humans use sensor-equipped robots to perform a simulated
reconnaissance exercise.
The theoretical framework reported herein extends work on robotic exploration
that has been reported by ourselves and others. Several new figures of merit
for evaluating exploration strategies are proposed and compared. Using concepts
from differential topology and information theory, we develop the theoretical
foundation of search strategies aimed at rapid discovery of topological
features (locations of critical points and critical level sets) of a priori
unknown differentiable random fields. The theory enables study of efficient
reconnaissance strategies in which the tradeoff between speed and accuracy can
be understood. The proposed approach to rapid discovery of topological features
has led in a natural way to to the creation of parsimonious reconnaissance
routines that do not rely on any prior knowledge of the environment. The design
of topology-guided search protocols uses a mathematical framework that
quantifies the relationship between what is discovered and what remains to be
discovered. The quantification rests on an information theory inspired model
whose properties allow us to treat search as a problem in optimal information
acquisition. A central theme in this approach is that "conservative" and
"aggressive" search strategies can be precisely defined, and search decisions
regarding "exploration" vs. "exploitation" choices are informed by the rate at
which the information metric is changing.Comment: 34 pages, 20 figure
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