Introduction to AI Principles: Search Strategies Overview

1. Introduction to AI &AI Principles (Semester 1)WEEK 10 (07/08) [John Barnden’s slides only] School of Computer Science University of Birmingham, UK

2. SEARCH • You should get the details from readings: • Callan book: Chapter 3 • John Bullinaria’s slides (week 8)

3. REVIEW: Towards “Search” • In planning, one can mentally “search” through possible states of the world you could get to, or that would be useful to get to, by imagining doing actions. • (FORWARDS SEARCH) If I do this, then that would happen, and then if I do this, that would come about, or if instead I did this then that would happen, … … … … … … … • OR • (BACKWARDS SEARCH) To get such and such a (sub-)goal state, I could perhaps do this action from such and such another state, and to get to that state I could perhaps do so-and-so, or alternatively I could have done such and such … … … …

4. REVIEW: Towards Search, contd. • What order to investigate the actions possible in or towards any given state? Investigate all or just some? All in a bunch, or at different points in the search? • Follow a line of investigation as far as you can, and then hop back to a choice point if not getting anywhere? • Any limit on the number of states investigated, or on how far you follow any given line? • How can you measure how promising a state is? • How to take care of unexpected world conditions or changes, or unexpected effects of your own actions?

5. New on Search • We have encountered search in at least the following forms: • The overall operation of production systems • Planning, navigation • Move/action choice, path choice, etc. in games • Evolutionary computing • Intersection search in semantic networks • Automatically finding good weights for a neural network (& you will see analogous forms of search in other machine learning schemes) • Finding a model of the environment in robot/vision systems.

6. Search, contd. • Search is a matter of investigating a set of possibilities, of any sort, by going from one to the other by means of certain specified operations. • Potentially important across all AI areas. • The “possibilities” are usually called “states”. They typically describe states of some world (real or artificial) outside the search, or some objects, or suggested solutions (partial or full) to a problem, or designs for something, etc. etc.

7. Search, contd. • A search problem consists of the following pre-defined things: • A set of states (infinitely many, possibly ). • A set of possible operations (finitely many, usually) that can transform states into other states, and a specification of their effects on states. • When operations represent things the agent could do in some environment represented by the states: a way of determining the cost of actually applying any particular sequence of operations starting from a particular state. • Cost usually found by adding up the costs of individual operations along the sequence. The cost is often just the sequence length. • A particular initial state, and either a particular goal state (or listed set of alternative goal states), or a goal condition: a specification of what goal state looks like. • In some cases, very many states, perhaps even all, count as goals: the question then is of getting to a best possible goal, according to some goal evaluation function. (This is the situation assumed by the “Hill Climbing” search strategy.)

8. Search, contd. • Possible aims in a search problem: • When only a goal condition is given: discover one or more specific goals (and perhaps a best possible goal). E.g.: a particular design for a building. • When either a specific goal state or a goal condition is given: discover one or more solution paths: each beinga sequence of operations going from the initial state to a goal state. • This is the more typical case. Applies to planning, for example. • In addition: usually want reasonably good solution paths in terms of length or cost; and may even want the optimal solution path(s).

9. Summary of Bullinaria’s Slides • Mainly about different overall search strategies, concerned with what order to look at states in, giving different “shapes” to the search process. The slides concentrate on forwards search. • Uninformed search strategies: the strategy does not use any knowledge other than the successor function (the algorithm for working out what effect an operation has on a state), the initial state, the goals and the cost function. • Informed search strategies: also use some heuristic algorithm that estimates how promising an operation is at a given state, or how promising a given state is from the point of view of reaching a goal, or how good as a goal a state is. The algorithm typically uses additional knowledge about the domain. • The algorithm is often in the form of a heuristic function: this estimates the remaining least possible cost from the state to a goal.

10. Bullinaria’s Slides, contd. • Uninformed strategies: centred on • Depth-first search • Breadth-first search (guarantees shortest solution paths) • Iterative deepening: compromise between depth-first and breadth-first. • Informed strategies: • Best-first search (uses heuristic function), & special case: • A* search (can guarantee optimal [=lowest cost] solution paths) • Hill Climbing.