Agents that plan

1. K. V. S. Prasad Notes for TIN171/DIT410 (Friday, 26 March 2010) Based on Nils Nilsson, “Artificial Intelligence: a new synthesis”, Morgan Kaufmann Publishers, 1998 Agents that plan

2. Agents that plan • Memory vs computation • State-space graphs • Searching explicit state spaces • Feature-based state spaces

3. Memory vs computation • Reactive agents • Do very little computation • actions selected by designer, or by learning, or genes • implemented in tables, or rules, or circuits • need lots of memory • Designer must anticipate all situation/reaction • Can the agent compute what the designer would? • Agent would then need more time but less space

4. Computations by the agent • Designer must specify these • not carry them out • E.g., predict the consequences of possible actions • If these can be learnt, or evolved • Agent does things designer did not anticipate • To predict consequences, agent must model • World • actions

5. World states and actions • Blocks world, make (ABC) from (A)(B)(C) • World modelled iconically • Actions by before-after pairs • move(A,B) • takes (A)(B)(C) to (AB)(C) • Takes (A)(BC) to (ABC) • Doesn't apply to (BA)(C) • So we can look ahead one step. To goal?

6. State-space graphs • Nodes = world states • Arcs = actions • (A)(B)(C) –move(A,B)--> (AB)(C) • In blocks world, all actions reversible • (AB)(C) –move(A,T)-->(A)(B)(C) • Where T is the table • So agent can see: • to go from (A)(B)(C) to (ABC) • do move(B,C), move(A,B)

7. Plan = path in state-space graph • Any of the nodes can be the goal • Sequence of actions needed (the plan) • Becomes path from initial to goal • Assumptions • Can represent all relevant world states and actions • No uncertainty in effect of actions • No other agent to change state • Then no sensor needed while acting

8. Searching explicit state-spaces • Start node is labelled 0 • Propagate numbers in waves along arcs • So arc labelled 3 has 3 step path back to initial • Wave go breadth-first; other sequences possible • Continue till you hit the goal • For single goal, can also back from goal to init

9. Feature-based state spaces • Nodes now labelled by features • Feature = logical proposition • Then what are actions? • STRIPS says an action is a triple • Precondition (what must be true for action to be possible) • Add list (what features become true after action) • Delete list (what features become false after action) • We are no longer in iconic world states!