Foundations

Getting a Handle on AI
- Some handwaving ideas [Coppin][RN][Luger]
  - Human(-like) results or not (superset? intersection?)
  - Human(-like) techniques or not (yes => human(-ilke) results) (no can still appear human(-like), or get different results)
  - Human(-like) hardware or not
  - Dispelling the artificial myth ... computational intelligence ... using computers, any techniques, any results.
- Getting a grip on "intelligence"
  - Class examples of intelligence
  - "the ability to solve problems is generally taken as a prime indicator that a system has intelligence" [NS p.X]
- An Intelligent Agent
  - Senses a problem (often requires intelligence)
  - Generates a solution (always requires intelligence)
    - Problem representation
    - Search for a solution
    - Knowledge for search guidance
  - Actuates the solution (occasionally requires intelligence)
  - The environment
    - An intelligent agent exists in an environment that contains problems that the agent solves.
    - The complexity of the environment may stimulate or require complex (intelligent) activity.
    - Provides the meaning for all problem solving activity
    - Is a source of knowledge that may be extracted by the sensors
Solving "hard" problems as intelligence
- Many things can be viewed as problems, from mathematical problems to spiritual satisfaction
- Solving problems - Initial state, state generator, solution detector
- Hard problems
  - Finding the Jamba Juice shop
  - O(E) and O(N^k) problems
- Solving easy O(N^k) problems is easy.
- Solving hard O(E) problems requires intelligence.
  - Random leaps of faith = O(E)
  - Exhaustive search = O(E)
  - With intelligence = O(N^k)
- Intelligence is the ability to solve O(E) problems with O(N^k) resources,
  through the use of guided search (making hard problems look easy)
- Representing the problem requires computer science, but no intelligence per se.
- Solving the problem by guiding the search is the intelligence
  - Knowledge in terms of the symbols (syntactic)
  - Knowledge as a model of the environment (semantic)
Emergent intelligence: Societies of agents
- Colonies exhibit intelligence (brains, ants, bakers, stock market) with limited individual agent intelligence
- Autonomous agents with specific capabilities
- Situated agents are aware of their part of the environment
- Communicative agents interact productively
- Structured societies are coordinated
- Societies are emergent - the whole is more intelligent than the sum of the parts.
- Need CS tools for these aspects

The Physical Symbol System Hypothesis - [CACM 19(3), pp.113-126]

Structure and composition
- Symbols
- Symbol structures
- Wider world of objects (physical and conceptual), including processes that manipulate symbol structures
Symbol structures designate processes and objects.
- The system can affect or react to the designated process or object.
- Processes and objects are different, but are designated by the same things (symbol structures).
- The thing that a symbol structure designates is determined by a separate mechanism. For symbol structures, the mechanism considers the arrangement of its constituent symbol structures.
- Designation provides the semantics of the symbol structures.
Interpretation
- A symbol structure designates a process by being the code that represents the process
- A symbol structure that designates a process can be executed
Completeness and closure
- A symbol may designate any object. Symbol structures designate objects according to their arrangement.
- Symbol structures are Turing complete
- There are processes for creating and manipulating symbol structures arbitarily.
- Symbols and symbol structures are stable.
- The system can hold infinite symbols and symbol structures (in principle).
The hypothesis: A physical symbol system has the necessary and sufficient means for general intelligent action.
- Any system that is intelligent turns out to be a physical symbol system.
- A physical symbol system of sufficient size can be organized to be intelligent.

Foundations [RN Ch.1.2]

Philosophy: algorithms, materialism, induction, backward reasoning
Mathematics: logic, computability, intractability, probability
Economics: utility, satisficing
Neuroscience: neurons, minds
Psychology: cognitive psychology, agents
Computer engineering: hardware performance, memory
Linguistics: Chomsky's theory, knowledge representation

AI areas [Lug Ch.1.2]

Natural Language Processing
Vision
Automated Reasoning
Game Playing
Planning
Knowledge Representation
Machine Learning
Robotics

Exam Style Questions

What are the three components of a classic definition of a problem?
Describe the operation of the Turing test as a criteria for success in artificial intelligence.
Draw a labelled diagram showing the components of an intelligent agent.
What facet of Philosophy suggests that the mind operates according to physical laws? What implications does this have for AI?
Name three important contributions to AI from Mathematics.
Describe how the rapid improvements in computer hardware have advanced the capabilities of AI systems.
Name and briefly (maximum 20 words each) describe N AI application areas.
The Physical Symbol System hypothesis states that "A Physical Symbol System has the necessary and sufficient means for general intelligent action." What is the structure of a Physical Symbol System?
In the context of physical symbol systems, what are meant by "designation" and "interpretation"?
Describe the five requirements of completeness and closure for a physical symbol system.