We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e.

Our key result is to verify that it is indeed possible to optimise control via targeted host removal by matching the “intrinsic scale of the epidemic” [35], selecting a cull radius that mini-mises the epidemic impact, KE (i.e.

We investigated how the nature of the optimum control strategy is conditioned on a selection of parameters controlling the host-pathogen interaction and the logistics of control.

Optimal control when there is uncertainty.

The quantitative detail of how these factors affect the nature of the optimal control strategy and how effectively it performs is extremely complex, and general principles remain ill-understood.

We consider a range of strategies for management of a newly invading plant pathogen, and identify optimal control scenarios that minimise the “epidemic impact”; we define this to be the total of both the number of hosts lost to disease and healthy hosts removed by control.

In particular, we show how the optimal control strategy changes when we take account of different levels of risk aversion [20].

long cryptic periods, lengthy delays between detection and tree removal), without optimal controls degenerating to immediate removal of the entire population at the time of the first control for our rather small population of interest.

Stochastic optimal control theory has proven quite successful at modeling goal-directed movements such as reaching [26], grasping [69] and saccades [70].

Despite the growing popularity of stochastic optimal control models, the preponderance of them are limited only to single goals.

In the current study we decompose the complex problem of action selection with competing alternatives into a mixture of optimal control systems that generate policies 71’ s to move the effector towards specif1c directions.

It combines dynamic neural field theory with stochastic optimal control theory, and includes circuitry for perception, eXpected reward, effort cost and decision-making.

By combining dynamic neural field theory with stochastic optimal control theory, we provide a principled way to understand how this competition takes place in the cerebral corteX for a variety of visuomotor decision tasks.

Each neuron in the motor plan formation DNF is linked with a stochastic optimal control schema that generates policies towards the preferred direction of the neuron.

By combining dynamic neural fields with stochastic optimal control systems the present framework explains a broad range of findings from experimental studies in both humans and animals, such as the influence of decision variables on the neuronal activity in parietal and premotor cortex areas, the effect of action competition on both motor and decision behavior, and the influence of effector competition on the neuronal activity in cortical areas that plan eye and hand movements.

The following sections describe how we integrate dynamic neural field theory with stochastic optimal control theory to develop a computational framework that can eXplain both neural and behavioral mechanisms underlying a wide variety of Visuomotor decision tasks.

It builds on successful models in dynamic neural field theory [25] and stochastic optimal control theory [26] and includes circuitry for perception, expected reward, selection bias, decision-making and effort cost.

Abrupt and gradual shifts in reach direction as a consequence of optimal control under goal uncertainty

We combined this decision-making process with an optimal control model of movement generation.

Although generally well accepted, these existing interpretations of intermediate movements are at odds with more contemporary theories of movement execution based on optimal control theory [20].

Here we show how intermediate movements can be understood within an optimal control framework if the control policy takes into account an evolving decision about the location of movement goals.

Recent advances in solution methods for optimal control problems [35] are inapplicable due to the structure of our control problem.

Optimal control theory has previously been invoked to account for intermediate movement strategies [36,37].

Solving this optimal control problem is not straightforward.

This precludes usual solution methods for optimal control problems which require an endpoint cost that is quadratic in the state.