Problem Solving agents

1. Problem Solving agents • Formulate Goal • Search for a solution • Execute the solution

2. Formulation problems • Knowledge and problem types • Single State problem • Multiple State problem • Contingency problem • Interleaving • Exploration problem • Well-defined problems • Initial state • Set of possible actions • Goal test • Path cost • Measuring solution performance • Problem/Action abstraction

3. Knowledge and problem types 1 2 3 4 5 6 7 8

4. Searching for Solutions • Generating action sequences Initial State Arad After Expanding Arad Arad Sibiu Timisoara Zerind After Expanding Sibiu Arad Sibiu Timisoara Zerind Arad Fagaras Oradea Rimnicu Vilcea

5. Search Algorithms • Breadth First • Searches across the tree • Always finds the shallowest goal state first. • Uniform Cost Search • Finds the ‘optimal’ solution • Depth First • Expands to the deepest level of each search • Depth-limited search • Iterative deepening search • Bi-directional Search • Searches forward from start state • Backward from goal state.

6. Avoiding Repeated States • Do not return to a previous state • Don not allow cyclic paths • Do not generate a state that was already been generated • Constraint Satisfaction Search • Constraints can be • Unary/Binary • absolute/preference • Variables in a CSP can be either • Continuous (Weight of a car’s component) • Discrete (It’s manufacturer)

7. CSP using Depth First • Variable CSP problems • Assigning any value to a variable results in • Naïve implementations assign values to any variable. • I|Di| • or 64 possibilities in the 8 queens problem • This can be limited by assigning values for a single variable at each node. • Thus limiting the choices to 8 • Applying this results in a search space of i|Di| • 88 in the case of the 8 queens problem • Back Tracking • Forward Checking