Fuzzy Semantics in Artificial Intelligence

Data Science, AI and ML
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The concept of fuzzy logic and fuzzy semantics is a central component to the programming of artificial intelligence solutions. Artificial intelligence solutions and tools continue to expand in the economy across a range of sectors as the programming capabilities from fuzzy logic also expand.

IBM’s Watson is one of the most well-known artificial intelligence systems using variations of fuzzy logic and fuzzy semantics. Specifically in financial services, fuzzy logic is being used in machine learning and technology systems supporting outputs of investment intelligence.

In some advanced trading models, the integration of fuzzy logic mathematics can also be used to help analysts create automated buy and sell signals. These systems help investors to react to a broad range of changing market variables that affect their investments.

Examples of Fuzzy Logic
In advanced software trading models, systems can use programmable fuzzy sets to analyze thousands of securities in real-time and present the investor with the best available opportunity. Fuzzy logic is often used when a trader seeks to make use of multiple factors for consideration. This can result in a narrowed analysis for trading decisions. Traders may also have the capability to program a variety of rules for enacting trades. Two examples include the following:

Rule 1: If the moving average is low and the Relative Strength Index (RSI) is low, then sell.
Rule 2: If the moving average is high and the Relative Strength Index (RSI) is high, then buy.
Fuzzy logic allows a trader to program their own subjective inferences on low and high in these basic examples to arrive at their own automated trading signals.