Exam Format

1
Exam Format

• 4 questions
• You must do all questions
• There is choice within some of the questions
• Learning Outcomes
• Explain the main approaches in classical planning
  as well as recent methods of planning
• Understand and discuss the advantages and
  limitations of these approaches
• Apply the presented planning algorithms to unseen
  examples

2
Questions

• Question 1
• Short essay discussing different planning
  technologies and their advantages and
  disadvantages
• LO2 Comparing different planners and discussing
  their strengths and weaknesses
• Questions 2 and 3
• Apply the planning algorithms we studied to a
  particular problem
• LO3 apply an algorithm to an unseen example
• You will have some choice about which algorithms
  to apply
• Do read the question carefully!

3
Questions, ctd.

• Question 4
• Short answers discussing particular planning
  algorithms and how they work
• Youll need to know about things like the Sussman
  anomaly, mutexes, partial order planning, etc
• Not details of the algorithms, but the ideas that
  are behind them
• LO1 explain a planning algorithm

4
Algorithms You Should Know

• The situation calculus
• Frame problem
• STRIPS
• Sussmann anomaly
• Non-Linear planning / Plan-space planning
• Graphplan
• SATPlan
• The Davis-Putnam procedure
• How to translate a planning problem into a SAT
  problem
• Hierarchical Planning
• Situation abstraction
• Operator abstraction
• HTNs

5
2006 Exam

• Describe the difference between a state-space and
  plan-space planner. What are their strengths and
  weaknesses? What kinds of problems (if any) can
  one solve but not the other?
• State-space planners build plans by searching in
  the graph of the state-transition system.
  Plan-space planners build plans by incrementally
  adding actions or constraints to a partial plan
• Because of this, a solution is a partial order
  for which all consistent total orders are
  executable plans
• Plan-space planners separate the actions in a
  plan from the order they are done in, so the
  control strategy of the planner has less
  influence on the kinds of plans that can be
  generated
• The plan space makes the reasons for plan
  structure explicit, so explanation is easier
• The state space is typically assumed to be
  finite, while the plan space is not
• The search space is simpler, so state-space
  planners are often faster, and scale better
• Both problems can in theory solve the same set
  of problems - the differences come from the
  specific search algorithms of planners, not from
  the state-space/plan-space approach. (2)

6
2006 Exam

• Hierarchical Task Networks (HTNs) are another
  planning approach that has been quite successful
  in some problems. Describe HTN planning, how it
  differs from classical planning approaches such
  as STRIPS, and suggest (with reasons) some
  domains in which it might be particularly
  appropriate.
• In classical planning the objective is to find a
  set of actions from a start state to a goal
  state. In HTN planning it is rather to provide a
  way of achieving a task or set of tasks. Tasks
  are divided into primitive tasks that can be
  executed directly, and non-primitive tasks which
  must be decomposed. Task decomposition is
  performed using a library of methods, each of
  which is a way to turn a more abstract task into
  a sequence of simpler tasks. Thus HTN planning
  differs from classical planners in that they are
  hierarchical rather than working in a flat space,
  and in the fact that the methods encode very
  specific information about solving problems, so
  the planning problem contains far more domain
  knowledge. HTN planners have been successful in
  areas where domain experts can fairly easily
  encode what needs to be done in terms of
  recipes'' to follow. A good example is in
  computer games.

7
2006 Exam Domain
8
2006 Exam

• Above is a planning domain involving driving
  vehicles and travelling between locations. Show
  in detail how STRIPS solves this problem. Assume
  optimal choices.
• Discuss the reasons why STRIPS is incomplete. Use
  examples to illustrate problems STRIPS cannot
  solve, and explain why not
• STRIPS only searches for actions to achieve the
  preconditions of the last operator added to the
  plan. This means that interleaving actions from
  different parts of the plan is impossible. For
  example, the switching variable values problem is
  unsolvable by STRIPS because it will make the
  first assignment, commit to the plan to solve
  that subgoal, and then be unable to make the
  second assignment as the value it needs to move
  has already been overwritten

9
2006 Exam

• Explain the approach used in planning as
  satisfiability algorithms such as SATPLAN.
  Contrast it with the approach in the situation
  calculus.
• The idea in SATplan is to define the initial and
  goal state, as well as all the operators in
  propositional logic, and use efficient SAT
  solvers to find a model of all formulae which
  then corresponds to a plan. SitCalc uses first
  order logic rather than propositional logic, and
  solves the problem by deductive reasoning, rather
  than simply finding an assignment of values to
  variables that satisfies the formula
• Show how the initial state and goal in the
  driving domain above could be represented as a
  satisfiability problem. How are the planning
  operators represented?
• At(Jack, here,0) ? At(Bulldozer, here,0) ?
  Vehicle(Bulldozer,0) ? Mobile(Jack, 0) ?
  Person(Jack,0) ?... ? At(Bulldozer, there,1) ?
  At(Jack, here, 1)
• In rules, all variables are instantiated by all
  possible values up to a particular evaluation
  depth, so all actions can be applied at any point
  in the plan. Each rule is an implication (if this
  is true at time t, then this is true at t1)

10
2006 Exam

• Draw a plan graph for this problem, showing all
  the mutexes, and the final plan found. Note that
  you do not need to show all the smaller plan
  graphs created before the final one.
• This version of the problem is unsolvable. Show
  the plan graph for this problem and explain how
  GraphPlan knows that there is no solution.
• What would STRIPS do on the problem in Part (b)?