1. AI – A Brief Overview

2. What is intelligence?
• Rational agent model
– Choosing among alternatives in such a way to maximize
achievement of goals within time and other resource constraints
• Ability to make accurate (enough) predictions
• Requires
– Ability to receive and process information
– Remember
– Learn and abstract from information
– Model
– Plan
– Act
– Evaluate progress

3. Strong AI vs Weak/Narrow AI
• Strong AI is general artificial intelligence
– In principle able to learn and act intelligently in a
broad general range, as humans can.
• Narrow AI
– Constrained in problem sets / domains
– Set of techniques for intelligent decisions / actions
– Ubiquitous across many software systems
– Does not attempt to solve the problem of general
intelligence
– Most AI today is narrow AI

4. Approaches to AI
• Brain emulation
• Brain simulation
• Symbolic
– Cognitive simulation
– Logic based
– Anti-logic or “scruffy”
– Knowledge-based
• Sub-symbolic
– Bottom up, embodied, situated
– Computational intelligence
• Neural networks
• Connectionist
– Evolutionary computation

5. Knowledge Acquisition
• Input Modalities
– Senses
• Vision
• Hearing
• Data communication
• Touch
• Accelerometers
• Other tech..
– Text/Video
• Linear modalities
• Speech recognition
• Natural language Processing
– Preassembled knowledge / data structures

6. Memory
• Temporal Memory
– Crucial to temporal reasoning
• Cause and affect inference
• Prediction
• Factual Memory
– Searchable fact stores
– Enabling inference
• Associative Memory
– Association between memories. How are memories and inferences strengthened or weakened
by new memories?
• Memory Trimming
– What is the proper tradeoff between detail and size/speed? How is saliency determined for
current and future goals? How does the memory structure cache and prune over time?
• Learning
– What can be inferred or generalized?
– What patterns and abstractions subsuming many facts and saving resources can be garnered?
• Search and Retrieval

7. Knowledge Representation
• Fundamental Goal
– Represent knowledge in a matter facilitating efficient, accurate
retrieval and reasoning
• Categories and Objects
– Categorization of objects is a basic central abstraction form and
greatly enhances efficiency
• Events
• Mental events and objects
• Reasoning Systems for Categories
– Semantic networks
– Description logics
• Reasoning with defaults
– Default facts are specified at category level and inherited

8. KR Categories
• Taxonomies
– Membership
– Relationships among categories
• Sub-categories
• Partitioning (exhaustive decomposition)
• Physical composition
– Part/whole
– Containment
• General relationships

9. KR - Events
• Event <= fluent, time
– A condition that varies over time at a particular
time or time period.
– So KR events are statements about facts
concerning referents in time
– Time-intervals and interval reasoning
– Time varying knowledge
• e.g., President(USA, time)

10. KR – Mental Events
• Beliefs
– Beliefs are internal states of the AI
• Subject to change with changing information
• Self-modeling
• internal state
• Knowledge about internal state of other
agents
• Modal logic

11. Inference
• Logical Inference
– Deduction
• Derives b from a where b is a formal consequence of a. Deriving consequences of what is
known or assumed.
– Induction
• Reasons from experience to an hypothesis generalizing experience. A “jumping to
conclusions”. Does not guarantee accuracy.
– Abduction
• Seeks plausible explanations or necessities for the facts to be as they are.
• Backward chaining from sought result to possible evidence.
• Backward chaining
– Starting from a goal and looking for conditions that support / infer that goal
recursively until known facts or sufficiently strong beliefs are found.
• Forward chaining
– A form of deductive inference chasing that may or may not converge on a
goal.

12. Goal Seeking
• Intelligence is difficult to speak of without goals, that which a
system seeks
• Goal seeking requires
– Goal formulation
– Problem formulation of what actions to consider in light of state and
goal
• Considers cost, likelihood of success of each possible action
– Performance measure
• An Intelligent agent optimizes its performance measurement
• Examples
– Touring problems
– Traveling salesman,
– Robot navigation
– Assembly sequencing

13. Basic Simple Search Techniques
• Exhaustive
– Can be exponential from combinatorial explosion but will find solution if exist
• Uniformed search
– No information for preferring one choice over another at each point
– Breadth first, uniform cost, depth first, depth limited iterative deepening, bi-
directional
• Informed (heuristic) search
– Greedy, best-first
– A* search
• Combines cost to reach node with distance from node to goal
– Memory bound heuristic search (combining iterative deepening with A*)
– Heuristic Sources
• Relaxing problem constraints
• Subproblem recognition from pattern database
• From experience

14. More sophisticated search
• Optimization problems
• Best state according to objective function (global maximum or minimum)
• Hill climbing search
• Simulated annealing
• Local beam search
• Genetic algorithm
– Successor states generate by combination of two or more states with modification
• Continuous space searches
• Searching with nondeterministic actions
– And-or trees
• Each node/action has several possible outcomes or range of outcomes (ands)
• Searching with incomplete perception
• Online search problems
– Real travel cost not just computational for each node traversed
• Depth first is best choice often
• Hill climbing is also workable
– Learning a map of the environment as it goes is important

15. Machine Learning Algorithm Types
• Supervised
– Input data is pre-labeled as to appropriate results. The learner approximates
the labeling function.
• Unsupervised
– Models set of inputs, like clustering
• Semi-supervised
– Combines labeled and unlabeled samples
• Reinforcement
– Learns due to feedback resulting from each attempt/guess. Common in neural
nets
• Transduction
– Tries to predict new outputs based on training inputs,outups and on test
inputs
• Learning to learn
– Learns its own inductive bias based on experience

16. Machine Learning Approaches
• Decision tree learning
• Association rule learning
• Artificial Neural networks
• Genetic programming
• Inductive logic programming
• Support Vector Machines
• Clustering
• Bayesian Networks
• Reinforcement Learning
• Representation Learning