Intelligent Systems Lecture Notes

Outline

Search over planning horizons.
For each planning horizon, create a CSP constraining possible actions and features.
Also factor actions into action features.

PUC: boolean, the agent picks up coffee.
DelC: boolean, the agent delivers coffee.
PUM: boolean, the agent picks up mail.
DelM: boolean, the agent delivers mail.
Move: variable with domain { mc, mac, nm } specifies whether the agent moves clockwise, anti-clockwise, or doesn’t move.

Choose a planning horizon k.
- Create a variable for each state feature and each time from 0 to k.
- Create a variable for each action feature for each time in the range 0 to k - 1.

State constraints: constraints between variables at the same time step.
Precondition constraints between state variables at time t and action variables at times t that specify constraints on what actions are available from a state.
Effect constraints between state variables at time t, action variables at time t and state variables at time t + 1.
Action constraints specify which actions cannot co-occur. These are sometimes called mutual exclusion or mutex constraints.
Initial state constraints that are usually domain constraints on the initial state (at time 0).
Goal constraints on the final state to be a state satisfying the goals to be advanced.

RHC_i DC_i PUC_i RHC_{i + 1}

t t t t

t t f f

t f t t

t f f t

f t t t

f t f f

f f t t

f t f f

Planning in IS (Chapter 12) in Intelligent Systems by Robert Schalkoff, Jones and Bartlett, 2011.
Classical Planning (Chapter 10) in Artifical Intelligence, third edition, by Stuart Russell and Peter Norvig, Prentice Hall, 2010.
Planning (Chapter 15) in Knowledge Representation and Reasoning by Ronald Brachman and Hector Levesque, Morgan Kaufmann, 2004.

This page last modified on 2011 October 9.