Biomedical subjects

Tim Kovacs

Publications and source records attributed to Tim Kovacs.

4 recordsLinked to original sources

PubMed2006

Noise, cost and speed-accuracy trade-offs: decision-making in a decentralized system.

Many natural and artificial decision-making systems face decision problems where there is an inherent compromise between two or more objectives. One such common compromise is between the speed and accuracy of a decision. The ability to exploit the characteristics of a decision problem in order to vary between the extremes of making maximally rapid, or maximally accurate decisions, is a useful property of such systems. Colonies of the ant Temnothorax albipennis (formerly Leptothorax albipennis) are a paradigmatic decentralized decision-making system, and have been shown flexibly to compromise accuracy for speed when making decisions during house-hunting. During emigration, a colony must typically evaluate and choose between several possible alternative new nest sites of differing quality. In this paper, we examine this speed-accuracy trade-off through modelling, and conclude that noise and time-cost of assessing alternative choices are likely to be significant for T. albipennis. Noise and cost of such assessments are likely to mean that T. albipennis' decision-making mechanism is Pareto-optimal in one crucial regard; increasing the willingness of individuals to change their decisions cannot improve collective accuracy overall without impairing speed. We propose that a decentralized control algorithm based on this emigration behaviour may be derived for applications in engineering domains and specify the characteristics of the problems to which it should be suited, based on our new results.

Algorithms↗

PubMed2006

A study of structural and parametric learning in XCS.

The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjusts parameters of existing rules, for example rule prediction and accuracy, in an evaluation step, which is not only important for applying the rules, but also for the genetic process. The two components interleave and in the case of XCS drive the population toward a minimal, fit, non-overlapping population. In this work we attempt to gain new insights as to the relative contributions of the two components. We find that the genetic component has an additional role when using the train/test approach which is not present in online learning. We compare XCS to a system in which the rule set is restricted to the initial random population (XCS-NGA, that is, XCS No Genetic Algorithm). For small Boolean functions we can give XCS-NGA all possible rules of a particular condition length. In online learning, XCS-NGA can, given sufficiently many rules, achieve a surprisingly high classification accuracy, comparable to that of XCS. In a train/test approach, however, XCS generalises better than XCS-NGA and there seem to be limitations of XCS-NGA which cannot be overcome simply by increasing the population size. This illustrates that the requirements of a function approximator tend to differ between reinforcement learning (which is typically online) and concept learning (which is typically train/test).

Algorithms↗

PubMed2004

Rule fitness and pathology in learning classifier systems.

It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation. In contrast, the more recent accuracy-based XCS, appears both to adapt and generalise well. In this work, we attribute the difference to what we call strong over general and fit over general rules. We begin by developing a taxonomy of rule types and considering the conditions under which they may occur. In order to do so an extreme simplification of the classifier system is made, which forces us toward qualitative rather than quantitative analysis. We begin with the basics, considering definitions for correct and incorrect actions, and then correct, incorrect, and overgeneral rules for both strength and accuracy-based fitness. The concept of strong overgeneral rules, which we claim are the Achilles' heel of strength-based classifier systems, are then analysed. It is shown that strong overgenerals depend on what we call biases in the reward function (or, in sequential tasks, the value function). We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS. Next we show how to design fit overgeneral rules for XCS (but not SB-XCS), by introducing biases in the variance of the reward function, and thus that each system has its own weakness. Finally, we give some consideration to the prevalence of reward and variance function bias, and note that non-trivial sequential tasks have highly biased value functions.

Algorithms↗

PubMed2003

Medical considerations in the use of helicopters in mountain rescue.

The outcome of patient care can be dramatically improved by bringing rapid rescue and medical care to the mountain rescue scene and by rapid transport to a medical facility. The use of a helicopter for these purposes is common. It is necessary when it has clear advantages for victims in comparison with ground rescue and transport. Helicopters should work within the existing emergency medical system and must be staffed by appropriate mountain rescue and medically trained personnel. Activation time should be as short as possible. Activation of a helicopter for a mountain rescue should primarily include indication and assessment of flight and safety conditions. No other mediators or delaying factors should be permitted. The main safety criteria are appropriate mountain rescue and flight training, competence of air and ground crews, radio communication between the air and ground crews, and mission briefing before the rescue. Criteria for a helicopter used for mountain rescue are proper medical and rescue equipment, load capacity, adequate space, and others. There are two main groups of indications for use of a helicopter for mountain rescue: the patient's condition and the circumstances at the site of the accident. All persons responsible for the activation of the helicopter rescue operation should be aware of specific problems in the mountains or wilderness.

Air Ambulances↗