Nonanticipatory (system or predictor) is a (system or predictor) where the output y(t) at some specific instant t₀ only depends on the input x(t) for values of t less than or equal to t₀. Therefore these kinds of (systems or predictors) have outputs and internal states that depend only on the current and previous input values.

In simpler words, nonanticipatory systems can “take into account” only past and present, and cannot base their behaviour/decisions on future expectations.

Nonanticipatory systems are also known as causal systems.

“… consider a universal predictor based on pattern matching: Given a sequence X_i,… ,X_n drawn from a stationary mixing source, it predicts the next symbol X_n+i based on selecting a context of X_n+i. The predictor, called the Sampled Pattern Matching (SPM), is a modification of the Ehrenfeucht-Mycielski pseudo random generator algorithm. It predicts the value of the most frequent symbol appearing at the so called sampled positions. These positions follow the occurrences of a fraction of the longest suffix of the original sequence that has another copy inside X_iX₂ … X_n. In other words, in SPM the context selection consists of taking certain fraction of the longest match. The study of the longest match for lossless data compression was initiated by [Aaron D.] Wyner and Ziv in their 1989 seminal paper.”
Read the rest of this entry »

(This series started with Pattern matching and prediction, part 1)

For part 2, I wanted to start (and probably also end) with Cybula’s AURA (universal pattern matcher, white-paper dated 2004). AURA is said to be built around Correlation Matrix Memory (CMM). CMMs were developed (or picked up for development?) by Prof. Austin, the founder of Cybula, in 1986.

The white paper tells us that

The now ubiquitous neural network methods such as Kohonen Networks, Radial Basis Function networks and Kohnen networks all allow users develop good pattern matching systems for small problems, where they excel. However, when the problems grow to large datasets, and where very high performance is needed, they become limited. … The well known k-Nearest Neighbour methods (k-NN) is a relatively good pattern matching method that has been constantly shown to operate well on many problems, however, it suffers from slow operation on large data problems.

Read the rest of this entry »

According to one of the definitions I provided earlier in the descriptive entry-level post on what is artificial intelligence, intelligence can be described as a special pattern-matching algorithm. Evidently, universal and complicated and recurring pattern matcher, but still just a pattern matcher

I decided to find out more about pattern matchers of nowadays… definitely not focusing too much on regular expressions, which are of no interest to me in the light of possible applications.
Read the rest of this entry »

Defining Artificial Intelligence was moved to Strong-AI.info.

In this post I’ll try to figure out (primarily for myself) what is Artificial Intelligence.
Read the rest of this entry »

What is an autonomous “intelligent” agent? moved to Strong-AI.info

In this post, some definitions and examples are given. This is an introductory text.

First of all there is a need to explain why “intelligent” is in braces in the title. Well, it’s simple: whatever the agents are at the moment of writing, they are just specific, narrow algorithms with no signs of intelligence. As soon as I come across the evidence of the contrary, I will happily remove the braces around “intelligent”. But for now – braces stay.

What is an agent? According to the numerous sources I checked, agent is an entity with some characteristic features. These fundamental features are:
Read the rest of this entry »