142 research outputs found
Indiana Wildlife Disease News, Volume 1, Issue 3 -- July 2006
Special points of interest: • Special Issue on rabies • Oral rabies vaccination zone • Update - avian influenza surveillance in wild birds in Indiana • Indiana Rabies Task Force • An update on wildlife disease in Indiana and surrounding states Inside this issue: Oral Rabies Vaccination 2 Mechanics of a Rabies Infection 2 Rabies in Indiana 3 Submitting Animals for Testing 4 AI Update Avian Botulism 5 Canine Distemper 5 Indiana Rabies Task Force
Improving games AI performance using grouped hierarchical level of detail
Computer games are increasingly making use of large environments; however, these are often only sparsely populated with autonomous agents. This is, in part, due to the computational cost of implementing behaviour functions for large numbers of agents.
In this paper we present an optimisation based on level of detail which reduces the overhead of modelling group behaviours, and facilitates the population of an expansive game world.
We consider an environment which is inhabited by many distinct groups of agents. Each group itself comprises individual agents, which are organised using a hierarchical tree structure. Expanding and collapsing nodes within each tree allows the efficient dynamic abstraction of individuals, depending on their proximity to the player. Each branching level represents a different level of detail, and the system is designed to trade off computational performance against behavioural fidelity in a way which is both efficient and seamless to the player.
We have developed an implementation of this technique, and used it to evaluate the associated performance benefits. Our experiments indicate a significant potential reduction in processing time, with the update for the entire AI system taking less than 1% of the time required for the same number of agents without optimisation
Novel Exploration Techniques (NETs) for Malaria Policy Interventions
The task of decision-making under uncertainty is daunting, especially for
problems which have significant complexity. Healthcare policy makers across the
globe are facing problems under challenging constraints, with limited tools to
help them make data driven decisions. In this work we frame the process of
finding an optimal malaria policy as a stochastic multi-armed bandit problem,
and implement three agent based strategies to explore the policy space. We
apply a Gaussian Process regression to the findings of each agent, both for
comparison and to account for stochastic results from simulating the spread of
malaria in a fixed population. The generated policy spaces are compared with
published results to give a direct reference with human expert decisions for
the same simulated population. Our novel approach provides a powerful resource
for policy makers, and a platform which can be readily extended to capture
future more nuanced policy spaces.Comment: Under-revie
Adversarial Imitation Learning from Incomplete Demonstrations
Imitation learning targets deriving a mapping from states to actions, a.k.a.
policy, from expert demonstrations. Existing methods for imitation learning
typically require any actions in the demonstrations to be fully available,
which is hard to ensure in real applications. Though algorithms for learning
with unobservable actions have been proposed, they focus solely on state
information and overlook the fact that the action sequence could still be
partially available and provide useful information for policy deriving. In this
paper, we propose a novel algorithm called Action-Guided Adversarial Imitation
Learning (AGAIL) that learns a policy from demonstrations with incomplete
action sequences, i.e., incomplete demonstrations. The core idea of AGAIL is to
separate demonstrations into state and action trajectories, and train a policy
with state trajectories while using actions as auxiliary information to guide
the training whenever applicable. Built upon the Generative Adversarial
Imitation Learning, AGAIL has three components: a generator, a discriminator,
and a guide. The generator learns a policy with rewards provided by the
discriminator, which tries to distinguish state distributions between
demonstrations and samples generated by the policy. The guide provides
additional rewards to the generator when demonstrated actions for specific
states are available. We compare AGAIL to other methods on benchmark tasks and
show that AGAIL consistently delivers comparable performance to the
state-of-the-art methods even when the action sequence in demonstrations is
only partially available.Comment: Accepted to International Joint Conference on Artificial Intelligence
(IJCAI-19
Top-k Multiclass SVM
Class ambiguity is typical in image classification problems with a large
number of classes. When classes are difficult to discriminate, it makes sense
to allow k guesses and evaluate classifiers based on the top-k error instead of
the standard zero-one loss. We propose top-k multiclass SVM as a direct method
to optimize for top-k performance. Our generalization of the well-known
multiclass SVM is based on a tight convex upper bound of the top-k error. We
propose a fast optimization scheme based on an efficient projection onto the
top-k simplex, which is of its own interest. Experiments on five datasets show
consistent improvements in top-k accuracy compared to various baselines.Comment: NIPS 201
Preliminary design specification for the LANDSAT Imagery Verification and Extraction System (LIVES)
There are no author-identified significant results in this report
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