Introduction
Military decision should consider information about a huge range of assets and capabilities
(Human resources combat and support vehicles, helicopters, sophisticated intelligence and
Communication equipment, artillery and missiles) that may perform complex tasks of multiple types: collection of intelligence, movements, direct/indirect fires, infrastructure, and transports.
The decisional factor needs an integrated framework capable to perform the critical steps,
From capturing a high-level course of action (CoA) to realising a detailed analysis/ plan of tasks
(Hayes, Schlabach 1998, Atkin, 1999, Tate, 2001, Kewley, Embrecht) and one possibility is to be Based on different AI techniques, ranging from qualitative spatial interpretation of CoA diagrams to interleaved adversarial scheduling.
A review of the possibilities to introduce AI algorithms in military applications
AI based military decision behavior models can be classified into the following groups: models based on neural networks (NN), Bayesian belief networks (BBN), fuzzy logic (FL), genetic
Algorithms (GA) and expert systems (ES).
Neural Networks applications
NN philosophy is based on the concept of a neuron as a unit for information storage and
mapping input to output. NNs are based on the connection of sets of simple processing elements/nodes, where a weight is associated to each connection between nodes. Weights are initialized randomly at the beginning, and as the network begins to learn, the weights change. The neuron receives a numerical input vector (binary or part of continuum) and each element of the input vectors scaled by a weighting constant, which assigns the importance rank to each input. The result of the dot product is used into a squashing function whose
output is used as the input to another neuron.
Genetic Algorithms
The classic genetic algorithm (GA) begins as a search technique for tackling complex problems.
Through the process of initialisation, selection, crossover, and mutation, GA repeatedly modifying a population of artificial structures in order to choose an appropriate structure for a particular problem. GAs is useful when the fitness landscape contains high, narrow peaks and
wide stretches of barren waste between them, GAs. If the area covered by fitness peaks approaches zero compared to the number of bad solutions in the landscape (good
solutions are exceedingly rare) a random problem solver will rarely find a good solution. Real worldfitness landscapes correspond to the difficult problems where traditional algorithms fail, and GAs should be applied to these problems.
Fuzzy Logic
Fuzzy Logic (FL) architecture consists of a set off fuzzy rules that expressed the relationship
between inputs and desired output. In these models inputs are fuzzyfied, membership functions are created, association between inputs and outputs are denied in a fuzzy rule base, and fuzzy outputs are restated as crisp values. Fuzzy rules in such a model could be provided by the decision maker (subjective fuzzy logic) or elicited from raw data (objective fuzzy logic).
Expert Systems
Expert systems (ES) use a knowledge base including a set of rules and an inference mechanism
that provides computer reasoning through inductive, deductive, or hybrid inductive- deductive
reasoning. Knowledge base rules usually are undertaken through interview with traders. Rules in such knowledge-based systems are represented in the form of computer readable sentences. Checking for consistency and validity of rules is essential for knowledge-based system, which is complex and difficult in the financial field, even when it is a system with only a dozen rules
Conclusions
The AI ingredient in military decision making offers a strong support capable to create natural
sketch-based interfaces that domain experts can use with low training. The users expressed the desire for a single integrated framework that capturesCoA sketches and statements simultaneously and capable to provide a unified map-based interface to do both tasks. The interest is to design a framework capable to express CoA sketches equipped with
visual understanding.
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