On the axes, you will find two macro-groups, i.e., the AI Paradigms and the AI Problem Domains. The AI Paradigms (X-axis) are the approaches used by AI researchers to solve specific AI-related problems (it includes up to date approaches). On the other side, the AI Problem Domains (Y-axis) are historically the type of problems AI can solve. In some sense, it also indicates the potential capabilities of an AI technology.
- Logic-based tools: tools that are used for knowledge representation and problem-solving
- Knowledge-based tools: tools based on ontologies and huge databases of notions, information, and rules
- Probabilistic methods: tools that allow agents to act in incomplete information scenarios
- Machine learning: tools that allow computers to learn from data
- Embodied intelligence: engineering toolbox, which assumes that a body (or at least a partial set of functions such as movement, perception, interaction, and visualization) is required for higher intelligence
- Search and optimization: tools that allow intelligent search with many possible solutions.
The vertical axis instead lays down the problems AI has been used for, and the classification here is quite standard:
- Reasoning: the capability to solve problems
- Knowledge: the ability to represent and understand the world
- Planning: the capability of setting and achieving goals
- Communication: the ability to understand language and communicate
- Perception: the ability to transform raw sensorial inputs (e.g., images, sounds, etc.) into usable information.