Page 9 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 1: Overview of Artificial Intelligence

Deduction, Reasoning and Problem Solving Notes

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans
use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research
had also developed highly successful methods for dealing with uncertain or incomplete
information, employing concepts from probability and economics.
For difficult problems, most of these algorithms can require enormous computational resources –
most experience a “combinatorial explosion”: the amount of memory or computer time required
becomes astronomical when the problem goes beyond a certain size. The search for more
efficient problem-solving algorithms is a high priority for AI research.
Human beings solve most of their problems using fast, intuitive judgments rather than the
conscious, step-by-step deduction that early AI research was able to model. AI has made some
progress at imitating this kind of “sub-symbolic” problem solving: embodied agent approaches
emphasize the importance of sensory motor skills to higher reasoning; neural net research
attempts to simulate the structures inside the brain that give rise to this skill; statistical approaches
to AI mimic the probabilistic nature of the human ability to guess.

Knowledge Representation

Ontology represents knowledge as a set of concepts within a domain and the relationships
between those concepts.

Figure 1.1: Knowledge Representation

Entity

Set Item

Individual Category

Abstract Concrete Spacetime

relator Property Occurrent Presential

Knowledge representation and knowledge engineering are central to AI research. Many of the
problems machines are expected to solve will require extensive knowledge about the world.
Among the things that AI needs to represent are: objects, properties, categories and relations
between objects; situations, events, states and time; causes and effects; knowledge about
knowledge and many other, less well researched domains. A representation of “what exists” is
ontology: the set of objects, relations, concepts and so on that the machine knows about. The
most general are called upper ontologism, which attempt provides a foundation for all other
knowledge.

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