Introduction to AI & AI Principles (Semester 1) WEEK 11 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK

Review of the term Search Evaluation forms

REVIEW of the term

Review: Topics I Have Covered uNature of AI: aims, applications, branches, issues. u“Intelligence” and its connection to “stupidity”. uExpert AI versus Everyday (“Common-Sense”) AI. uWhy everyday AI is difficult. l Language processing, vision, planning, common-sense reasoning u Search (and more on this today).

Review: My Topics, contd uWhy planning, common-sense reasoning, language processing, etc. may need representation. uWhy natural language is problematic for this … while also having many strengths. uWhat we need to represent: entities (incl. situations, feelings, …), properties, relationships, groups, propositional structure, generalization/quantification, …

Review: My Topics, contd u Taster of logic. l Captures entities, properties, relations, extreme forms of quantification, basic forms of propositional structure. Can also handle groups of entities. l Aims of logic: clarity and simplicity compared to NL; systematic, sound reasoning; general applicability; common format for comparison. uIntro to semantic networks (and frames). uProduction systems.

Review: Guest Lectures uChess, Computer games uLearning, Neural networks uEvolutionary computing uVision uRobotics, Agents uPhilosophy

Review: General Themes uUncertainty, vagueness, conflict, missing info, diversity of info. uHence: satisficing, graceful degradation, heuristic processing (i.e., using rules of thumb). uContext-sensitivity; relativity to agents’ purposes. uTask variability, learning, adaptation, repair (e.g., of plans). uRepresentation. uReasoning. uSearch (more in a minute).

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

REVIEW: Towards “Search” uIn 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 … … … …

REVIEW: Towards Search, contd. uWhat 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? uFollow a line of investigation as far as you can, and then hop back to a choice point if not getting anywhere? uAny limit on the number of states investigated, or on how far you follow any given line? uHow can you measure how promising a state is? uHow to take care of unexpected world conditions or changes, or unexpected effects of your own actions?

New on Search uWe have encountered search in at least the following forms, apart from in planning/navigation: l Move choice in chess l Evolutionary computing l Intersection search in semantic networks l The overall operation of production systems l Common-sense reasoning tasks such as choosing a good set of presents to give to people.

Search, contd. uSearch 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. uPotentially important across all AI areas. uThe “possibilities” are usually called “states”. They typically describe states of some world (real or artificial) outside the search, or some objects, or designs for something, etc. etc.

Search, contd. uA search problem consists of the following pre-defined things: l A set of states (infinitely many, possibly ). l A set of possible operations (finitely many, usually) that can transform states into other states, and a specification of their effects on states. l 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. l 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. l 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.)

Search, contd. uPossible aims in a search problem: l 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. l When either a specific goal state or a goal condition is given: discover one or more solution paths: each being a sequence of operations going from the initial state to a goal state. This is the more typical case. Applies to planning, for example. l In addition: usually want reasonably good solution paths in terms of length or cost; and may even want the optimal solution path(s).

Summary of Bullinaria’s Slides uMainly 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. uUninformed 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. uInformed 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.

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