Learning automaton

Key takeaways

Learning automaton belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Learning automaton to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Learning automaton from memory before moving on to harder problems.

Reference excerpt

A learning automaton is one type of machine learning algorithm studied since 1970s. Learning automata select their current action based on past experiences from the environment. It will fall into the range of reinforcement learning if the environment is stochastic and a Markov decision process (MDP) is used.

History Research in learning automata can be traced back to the work of Michael Lvovitch Tsetlin in the early 1960s in the Soviet Union. Together with some colleagues, he published a collection of papers on how to use matrices to describe automata functions. Additionally, Tsetlin worked on reasonable and collective automata behaviour, and on automata games. Learning automata were also investigated by researches in the United States in the 1960s. However, the term learning automaton was not used until Narendra and Thathachar introduced it in a survey paper in 1974.

Definition A learning automaton is an adaptive decision-making unit situated in a random environment that learns the optimal action through repeated interactions with its environment. The actions are chosen according to a specific probability distribution which is updated based on the environment response the automaton obtains by performing a particular action. With respect to the field of reinforcement learning, learning automata are characterized as policy iterators. In contrast to other reinforcement learners, policy iterators directly manipulate the policy π. Another example for policy iterators are evolutionary algorithms. Formally, Narendra and Thathachar define a stochastic automaton to consist of:

a set X of possible inputs, a set Φ = { Φ1, ..., Φs } of possible internal states, a set α = { α1, ..., αr } of possible outputs, or actions, with r ≤ s, an initial state probability vector p(0) = ≪ p1(0), ..., ps(0) ≫, a computable function A which after each time step t generates p(t+1) from p(t), the current input, and the current state, and a function G: Φ → α which generates the output at each time step. In their paper, they investigate only stochastic automata with r = s and G being bijective, allowing them to confuse actions and states. The states of such an automaton correspond to the states of a "discrete-state discrete-parameter Markov process". At each time step t=0,1,2,3,..., the automaton reads an input from its environment, updates p(t) to p(t+1) by A, randomly chooses a successor state according to the probabilities p(t+1) and outputs the corresponding action. The automaton's environment, in turn, reads the action and sends the next input to the automaton. Frequently, the input set X = { 0,1 } is used, with 0 and 1 corresponding to a nonpenalty and a penalty response of the environment, respectively; in this case, the automaton should learn to minimize the number of penalty responses, and the feedback loop of automaton and environment is called a "P-model". More generally, a "Q-model" allows an arbitrary finite input set X, and an "S-model" uses the interval [0,1] of real numbers as X. A visualised demo/ Art Work of a single Learning Automaton had been developed by μSystems (microSystems) Research Group at Newcastle University.

Finite action-set learning automata Finite action-set learning automata (FALA) are a class of learning automata for which the number of possible actions is finite or, in more mathematical terms, for which the size of the action-set is finite.

See also Reinforcement learning Game theory Automata theory

Literature Philip Aranzulla and John Mellor (Home page): Mellor J and Aranzulla P (2000): "Using an S-Model Response Environment with Learnng [sic] Automata Based Routing Schemes for IP Networks ", Proc. Eighth IFIP Workshop on Performance Modelling and Evaluation of ATM and IP Networks, pp 56/1-56/12, Ilkley, UK. Aranzulla P and Mellor J (1997): "Comparing two routing algorithms requiring reduced signalling when applied to ATM networks", Proc. Fourteenth UK Teletraffic Symposium on Performance Engineering in Information Systems, pp 20/1-20/4, UMIST, Manchester, UK. Narendra K.; Thathachar M.A.L. (July 1974). "Learning automata – a survey" (PDF). IEEE Transactions on Systems, Man, and Cybernetics. SMC-4 (4): 323–334. CiteSeerX 10.1.1.295.2280. doi:10.1109/tsmc.1974.5408453. {{cite journal}}: Cite uses deprecated parameter |citeseerx= (help) Tsetlin M.L. Automation theory and modeling of biological systems. Academic Press; 1973.

References

Worked examples

Example 1 — a first encounter with Learning automaton

Start with the simplest possible case. Write down what Learning automaton claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Learning automaton before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Learning automaton ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Learning automaton

In research: Learning automaton appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Learning automaton in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Learning automaton is common in secondary-school and first-year university syllabi. It links to neighbouring topics Control theory, Machine learning, so understanding it makes those chapters shorter.
In everyday life: Look for Learning automaton outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Learning automaton in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Learning automaton means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Learning automaton out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Learning automaton in simple terms?

A learning automaton is one type of machine learning algorithm studied since 1970s. Learning automata select their current action based on past experiences from the environment.

Why does Learning automaton matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Learning automaton?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Learning automaton.