KBAI

Cognative Systems

Intro to KBAI

Characteristics of AI Problems

Knowledge arrives incrementally
Problems exhibit recurring patterns
Problems have multiple levels of granularity
Problems are computationall intractable
The world is dynamic, but knowledge is static
The world is open-ended, but knowledge is finite

Characteristics of AI Agents

Limited computing power
Limited sensors
Limited attention
Computational logic is fundamentally deductive
Agent knowledge is incomplete relative to the world

Knowledge Representations

Semantic Networks

Problem Solving Techniques

Generate & Test

Architectures

Fundamental Processes of KBAI Cognative Systems

"Cognative" means dealing with human-like intelligence
Cognative system and KBAI-agent are used interchangably
Deliberation, Meta-Cognition and Reaction make the three-layer system

Deliberation

Reasoning about the world around us.

Reasoning
Learning
Memory: Knowledge gained from learning needs to be stored

think of being in traffic and wanting to change lanes. Requires planning

Meta-cognition

Reasoning about deliberation or reaction.

Lane-change results in another car honking at you. You need to reason about the cause of the sub-optimal result.

Reaction

A direct mapping from perception to action

Kinds of KBAI

Think/Act
Optimally/Like Humans
Think like humans: Semantic Web
Think optimally: Machine Learning
Act like humans: Improvisational Robotics
Act optimally: Airplane Auto-pilot

Principles of KBAI

KBAI agents represent and organize knowledge into knowledge structures to guide and support reasoning
Learning in KBAI agents is often incremental
Reasoning in KBAI agents is top-down as well as bottom-up
KBAI agents match methods to tasks
KBAI agents use heuristics to find solutions that are good enough, though not necessarily optimal
KBAI agenbts make use of recurring patterns in the problems they solve
The architecture of KBAI agents enables reasoning, learning and memory to support and constrain each other

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