1. Please Also Complete Teaching Evaluations
TA Evaluations
Please Also Complete Teaching Evaluations
Johnson, David
Johnson, Jordon
will close on Sun, June 25th
Kazemi, Seyed Mehran
Rahman, MD Abed
Wang, Wenyi
Dylan Dong
Johnson, David
Johnson, Jordon
Kazemi, Seyed Mehran
Rahman, MD Abed
Wang, Wenyi

2. Decision Theory: Sequential Decisions
Computer Science cpsc322, Lecture 34
(Textbook Chpt 9.3)
June, 22, 2017
Lecture 34

3. “Single” Action vs. Sequence of Actions
Set of primitive decisions that can be treated as a single macro decision to be made before acting
Agent makes observations
Decides on an action
Carries out the action
One-off decisions
Sequential Decisions

4. Finding Optimal Policies
Lecture Overview
Sequential Decisions
Representation
Policies
Finding Optimal Policies
This extends single-stage decision networks by allowing both chance nodes and decision nodes to be parents of decision nodes. Arcs coming into decision nodes represent the information that will be available when the decision is made. Arcs coming into chance nodes represents probabilistic dependence. Arcs coming into the utility node represent what the utility depends on

5. Sequential decision problems
A sequential decision problem consists of a sequence of decision variables D1 ,…..,Dn.
Each Di has an information set of variables pDi, whose value will be known at the time decision Di is made.
Example syntactic
Simplest <-> degenerate case
Weather Forecast Umbrella

6. Sequential decisions : Simplest possible
Only one decision! (but different from one-off decisions)
Early in the morning. I listen to the weather forecast, shall I take my umbrella today? (I’ll have to go for a long walk at noon)
What is a reasonable decision network ?
Morning
Forecast
Morning
Forecast U U
Example syntactic
Simplest <-> degenerate case
Weather Forecast Umbrella
Take Umbrella
Take Umbrella A.
C . None of these B.

7. Sequential decisions : Simplest possible
Only one decision! (but different from one-off decisions)
Early in the morning. Shall I take my umbrella today? (I’ll have to go for a long walk at noon)
Relevant Random Variables?
Example syntactic
Simplest <-> degenerate case
Weather Forecast Umbrella

8. Policies for Sequential Decision Problem: Intro
A policy specifies what an agent should do under each circumstance (for each decision, consider the parents of the decision node)
In the Umbrella “degenerate” case: D1
One possible Policy
pD1
How many policies?

9. Sequential decision problems: “complete” Example
A sequential decision problem consists of a sequence of decision variables D1 ,…..,Dn.
Each Di has an information set of variables pDi, whose value will be known at the time decision Di is made.
What should an agent do?
What an agent should do at any time depends on what it will do in the future.
What an agent does in the future depends on what it did before.
No-forgetting decision network:
decisions are totally ordered
if a decision Db comes before Da ,then
Db is a parent of Da
any parent of Db is a parent of Da

10. Policies for Sequential Decision Problems
A policy is a sequence of δ1 ,….., δn decision functions
δi : dom(pDi ) → dom(Di )
This policy means that when the agent has observed
O  dom(pDi ) , it will do δi(O)
Example:
Report
Check Smoke
Report CheckSmoke SeeSmoke
Call
true true true
true true false
true false true
true false false
false true true
false true false
false false true
false false false
true
false
Check smoke false and See smoke true is impossible so that entry will never occur
How many policies?

11. Finding Optimal Policies
Lecture Overview
Recap
Sequential Decisions
Finding Optimal Policies

12. When does a possible world satisfy a policy?
A possible world specifies a value for each random variable and each decision variable.
Possible world w satisfies policy δ , written w ╞ δ if the value of each decision variable is the value selected by its decision function in the policy (when applied in w).
Decision function for…
Report
Check Smoke
true
false
VARs
Fire
Tampering
Alarm
Leaving
Report
Smoke
SeeSmoke
CheckSmoke
Call
true
false
true true
Decision function for…
Let’s parse this together!
Report CheckSmoke SeeSmoke
Call
true true true
true true false
true false true
true false false
false true true
false true false
false false true
false false false
true
false

13. When does a possible world satisfy a policy?
Possible world w satisfies policy δ , written w ╞ δ if the value of each decision variable is the value selected by its decision function in the policy (when applied in w).
Decision function for…
Report
Check Smoke
true
false
VARs
Fire
Tampering
Alarm
Leaving
Report
Smoke
SeeSmoke
CheckSmoke
Call
true
false
true true
Decision function for…
Report CheckSmoke SeeSmoke
Call
true true true
true true false
true false true
true false false
false true true
false true false
false false true
false false false
true
false
Let’s parse this together! A. B.
C . Cannot tell

14. Expected Value of a Policy
Each possible world w has a probability P(w) and a utility U(w)
The expected utility of policy δ is
The optimal policy is one with the expected utility.

15. Finding Optimal Policies (Efficiently)
Lecture Overview
Recap
Sequential Decisions
Finding Optimal Policies (Efficiently)

16. Complexity of finding the optimal policy: how many policies?
How many assignments to parents?
How many decision functions? (binary decisions)
How many policies?
If a decision D has k binary parents, how many assignments of values to the parents are there?
If there are b possible actions (possible values for D), how many different decision functions are there?
If there are d decisions, each with k binary parents and b possible actions, how many policies are there? 2k
b2k
(b2k)d

17. Finding the optimal policy more efficiently: VE
Create a factor for each conditional probability table and a factor for the utility.
Sum out random variables that are not parents of a decision node.
Eliminate (aka sum out) the decision variables
Sum out the remaining random variables.
Multiply the factors: this is the expected utility of the optimal policy.
Finally, there are a few restrictions in place for decision networks. First, there may only be one utility node. Also, the utility of that node and the probabilities of any nodes which have decision nodes as parents are undefined until the decision functions for their parents are defined. A final restriction is that a decision may not be optimized if one of its parents has an observed value. A decision function defines the agent's actions for any context. Thus, if some decisions are observed or have observed parents, you will not be able to optimize them. It cannot find a decision function for any context because it is limited by its parent's observed value.

18. Eliminate the decision Variables: step3 details
Select a variable D that corresponds to the latest decision to be made
this variable will appear in only one factor with its parents
Eliminate D by maximizing. This returns:
A new factor to use in VE, maxD f
The optimal decision function for D, arg maxD f
Repeat till there are no more decision nodes.
Report
Value
true
false
New factor
Example: Eliminate CheckSmoke
this variable will appear in only one factor with its parents
Because of the no-forgetting assumption the parents of the latest decision to be made will include all the
Parents of the other decisions and so all the vars that may influence the utility factor (which is the one/only factor
that will contain the latest decision)
And also because we have eliminated all the random variables that are not parents of a decision var
(so vars that could be “downstream” from the latest decision to be made)
Report CheckSmoke
Value
true true
true false
false true
false false
-5.0
-5.6
-23.7
-17.5
Decision Function
Report
CheckSmoke
true false

19. VE elimination reduces complexity of finding the optimal policy
We have seen that, if a decision D has k binary parents, there are b possible actions, If there are d decisions,
Then there are: (b 2k)d policies
Doing variable elimination lets us find the optimal policy after considering only d .b 2k policies (we eliminate one decision at a time)
VE is much more efficient than searching through policy space.
However, this complexity is still doubly-exponential we'll only be able to handle relatively small problems.

21. Learning Goals for today’s class
You can:
Represent sequential decision problems as decision networks. And explain the non forgetting property
Verify whether a possible world satisfies a policy and define the expected value of a policy
Compute the number of policies for a decision problem
Compute the optimal policy by Variable Elimination
CPSC 322, Lecture 4

22. Big Picture: Planning under Uncertainty
Probability Theory
Decision Theory
One-Off Decisions/ Sequential Decisions
Markov Decision Processes (MDPs)
Fully Observable MDPs
Partially Observable MDPs (POMDPs)
Decision Support Systems (medicine, business, …)
Economics
Control Systems
Robotics

23. Constraint Satisfaction
Cpsc 322 Big Picture
Environment
Stochastic
Deterministic
Problem
Arc Consistency
Search
Constraint Satisfaction
Vars +
Constraints
SLS
Static
Belief Nets
Logics
Query
Var. Elimination
Search
Markov Chains
R&R Sys Representation and reasoning Systems
Each cell is a R&R system
STRIPS actions preconditions and effects
Sequential
STRIPS
Decision Nets
Planning
Var. Elimination
Search
Representation
Reasoning
Technique
CPSC 322, Lecture 2

24. Applications of AI 422 big picture Query Planning Deterministic
Stochastic
Value Iteration
Approx. Inference
Full Resolution
SAT
Logics
Belief Nets
Markov Decision Processes and
Partially Observable MDP
Markov Chains and HMMs
First Order Logics
Ontologies
Applications of AI
Approx. : Gibbs
Undirected Graphical Models
Markov Networks
Conditional Random Fields
Reinforcement Learning
Representation
Reasoning
Technique
Prob CFG
Prob Relational Models
Markov Logics
StarAI (statistical relational AI)
Hybrid: Det +Sto
Forward, Viterbi….
Approx. : Particle Filtering
422 big picture
Breadth and Depth
CPSC 422, Lecture 35

25. More AI .. Machine Learning Knowledge Acquisition
StarAI (statistical relational AI)
Hybrid: Det +Sto
Machine Learning
Knowledge Acquisition
Preference Elicitation
Prob CFG
Prob Relational Models
Markov Logics
Deterministic
Stochastic
Belief Nets
Logics
Where are the components of our representations coming from?
First Order Logics
Markov Chains and HMMs
Ontologies
Query
Undirected Graphical Models
Markov Networks
Conditional Random Fields
The probabilities?
The utilities?
The logical formulas?
R&R Sys Representation and reasoning Systems
Each cell is a R&R system
Markov Decision Processes and
Partially Observable MDP
Planning
From people and from data!
Reinforcement Learning
CPSC 422, Lecture 35

26. Some of our Grad Courses
522: Artificial Intelligence II : Reasoning and Acting Under Uncertainty Sample Advanced Topics….. Relational Reinforcement Learning for Agents in Worlds with Objects, relational learning. - Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale,
CPSC 422, Lecture 35

27. Some of our Grad Courses
503: Computational Linguistics I / Natural Language Processing
 Sample Advanced Topics…..
Topic Modeling (LDA) – Large Scale Graphical Models
Discourse Parsing by Deep Learning (Neural Nets)
Abstractive Summarization
CPSC 422, Lecture 35

28. Other AI Grad Courses: check them out
532: Topics in Artificial Intelligence (different courses)
User-Adaptive Systems and Intelligent Learning Environments
Foundations of Multiagent Systems
540: Machine Learning
505: Image Understanding I: Image Analysis  525: Image Understanding II: Scene Analysis 
515: Computational Robotics 
CPSC 422, Lecture 35

29. Announcements
Assignment 4. Due Sunday, June 11:59 pm.  Late submissions will not be accepted, and late days may not be used..
FINAL EXAM: Thu, Jun 29 at 7-9:30 PM Room: BUCH A101
Final will comprise: short questions problems
Work on all practice exercises (including 9.B) and sample review questions and problems (will be posted over the weekend)
While you revise the learning goals, work on review questions - I may even reuse some verbatim 
Come to remaining Office hours! (schedule for next week will be posted on piazza) My office hour tomorrow will be at 2PM
CPSC 322, Lecture 37