Heuristics are rules of thumb or simplified strategies that lead to a good, though not guaranteed optimal, solution within reasonable time. In computer science and AI they help narrow down huge search spaces – for example in route planning, where the straight-line distance to the destination serves as an estimate, or in chess, where positions are evaluated. People also use heuristics in everyday life and business decisions, with the risk of systematic thinking errors.
An expert system is a computer program that stores the know-how of human specialists as rules and facts and draws conclusions from them. It typically consists of a knowledge base and an inference engine that applies the rules. Expert systems shaped AI in the 1970s and 1980s, for example in medical diagnosis or configuring technical equipment. Their ideas live on today in rule-based checks and knowledge graphs.
A decision tree is a model that represents a decision as a sequence of simple yes/no questions: each branch tests one feature and each leaf gives an outcome. In machine learning, such trees are learnt automatically from data and used for classification or forecasting. Their main strength is transparency – for instance, showing why a loan application was flagged based on income, term and payment history.
Monte Carlo methods are computational techniques that use a very large number of random samples to approximate results that are hard to calculate analytically. A system is simulated thousands of times with randomly varied assumptions, and the distribution of outcomes is analysed. Organisations use this for risk analysis, project scheduling or portfolio valuation. In AI, the methods are used in reinforcement learning and in tree search for games, among other areas.
Knowledge engineering covers capturing, organising and preparing expert knowledge so that IT systems can use it. Originally it focused on rules for expert systems; today the priority is often to document scattered knowledge from manuals, process descriptions and people's heads so that AI agents can answer reliably from it. This involves clear structures, up-to-date sources, defined responsibilities and closing identified gaps.
