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DCP 1172 Introduction to Artificial Intelligence Chang-Sheng Chen Topics Covered: • Review of DCP DCP 1172 Introduction to Artificial Intelligence Chang-Sheng Chen Topics Covered: • Review of DCP 1172 1

Summary/Review • Introduction to Artificial Intelligence • Problem-solving • Problem formulation • CSP, Game Summary/Review • Introduction to Artificial Intelligence • Problem-solving • Problem formulation • CSP, Game Search, Planning, …etc. • Search • Best-first search, A*, GA, etc. • Heuristics • local search, Means-ends analysis, etc. • Knowledge Representation • Logic/Ontology/Computation model • Rules/Semantic networks/Frame/… etc • Logical Reasoning • • Propositional logic, predicate logic Forward reasoning, backward reasoning Logic programming Monotonic reasoning, non-monotonic reasoning DCP 1172, Final 2

Classification of knowledge systems KNOWLEDGE SYSTEMS Symbolic (derivability, soundness, completeness) Rules (effcient computabiliy) Semantic Classification of knowledge systems KNOWLEDGE SYSTEMS Symbolic (derivability, soundness, completeness) Rules (effcient computabiliy) Semantic networks (eloquence, simplicity) DCP 1172, Final Frames (modularity, inheritance) 3

Representation we have included • • Propositional and predicate logic Semantic nets Search trees Representation we have included • • Propositional and predicate logic Semantic nets Search trees frames DCP 1172, Final 4

Knowledge-Based Agents § A knowledge representation is a formal scheme that dictates how an Knowledge-Based Agents § A knowledge representation is a formal scheme that dictates how an agent is going to represent its knowledge in the knowledge base. • Syntax (語法): Rules that determine the possible strings in the language. • Semantics(語意): Rules that determine a mapping from sentences in the representation to situations in the world. §. Knowledge Representation = Logic + Ontology + Computation DCP 1172, Final 5

Knowledge Representation = Logic + Ontology + Computation • Knowledge representation (KR) is a Knowledge Representation = Logic + Ontology + Computation • Knowledge representation (KR) is a multidisciplinary subject that applies theories and techniques from three fields: § Logic • provides the formal structure and rules of inference. • Ontology • defines the kinds of thins that exist in the application domain. • Computation • supports the applications that distinguish knowledge representation from pure philosophy. DCP 1172, Final 6

KR = Logic + Ontology + Computation (cont. ) • Knowledge representation is the KR = Logic + Ontology + Computation (cont. ) • Knowledge representation is the application of logic and ontology to the task of constructing computable models for some domain. • Without logic, a knowledge representation is vague, with no criteria for determining whether statements are redundant or contradictory. • Without ontology, the terms and symbols are ill -defined, confused, and confusing. • Without computable models, the logic and ontology cannot be implemented in a computer program. DCP 1172, Final 7

Monotonic Reasoning • Monotonic reasoning • If a conclusion C can be derived from Monotonic Reasoning • Monotonic reasoning • If a conclusion C can be derived from a set of expressions, S, then any number of additional expressions being added to S cannot change the truth value of C, provided the expression in S remain consistent. • Propositional logic and predicate logic are monotonic reasoning systems. • In other words, a monotonic reasoning system that stores facts about the real world can deduce new facts from its existing facts but would never have cause to delete or modify an existing fact (unless the world changed). • Hence, the number of facts the system stores will increase monotonically. DCP 1172, Final 8

Review • Agents can use search to find useful actions based on looking into Review • Agents can use search to find useful actions based on looking into the future • Agents can use logic to complement search to represent and reason about • Unseen parts of the current environment • Past environments (where are my keys) • Future environments • And they can play a mean game of chess DCP 1172, Final 9

A Framework : What is AI? The exciting new effort to make computers thinks A Framework : What is AI? The exciting new effort to make computers thinks … machine with minds, in the full and literal sense” (Haugeland 1985) “The study of mental faculties through the use of computational models” (Charniak et al. 1985) “The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil, 1990) A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes” (Schalkol, 1990) Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally DCP 1172, Final 10

The Architectural Components of AI Systems • • • State-space search Knowledge representation Logical The Architectural Components of AI Systems • • • State-space search Knowledge representation Logical reasoning Reasoning under uncertainty [ Not covered ] Learning [Not covered] DCP 1172, Final 11

Acting Humanly: The Turing Test • Alan Turing's 1950 article Computing Machinery and Intelligence Acting Humanly: The Turing Test • Alan Turing's 1950 article Computing Machinery and Intelligence discussed conditions for considering a machine to be intelligent • “Can machines think? ” “Can machines behave intelligently? ” • The Turing test (The Imitation Game): Operational definition of intelligence. DCP 1172, Final 12

What would a computer need to pass the Turing test? (1) • Natural language What would a computer need to pass the Turing test? (1) • Natural language processing: to communicate with examiner. • Knowledge representation: to store and retrieve information provided before or during interrogation. • Automated reasoning: to use the stored information to answer questions and to draw new conclusions. • Machine learning: to adapt to new circumstances and to detect and extrapolate patterns. DCP 1172, Final 13

Acting Humanly: The Full Turing Test Problem: 1) Turing test is not reproducible, constructive, Acting Humanly: The Full Turing Test Problem: 1) Turing test is not reproducible, constructive, and amenable to mathematic analysis. 2) What about physical interaction with interrogator and environment? Trap door DCP 1172, Final 14

What would a computer need to pass the Turing test? (2) • Vision (for What would a computer need to pass the Turing test? (2) • Vision (for Total Turing test): to recognize the examiner’s actions and various objects presented by the examiner. • Motor control (total test): to act upon objects as requested. • Other senses (total test): such as audition, smell, touch, etc. DCP 1172, Final 15

Summary - Intelligent Agents • Intelligent Agents: • Anything that can be viewed as Summary - Intelligent Agents • Intelligent Agents: • Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its actuators to maximize progress towards its goals. • PAGE (Percepts, Actions, Goals, Environment) • Described as a Perception (sequence) to Action Mapping: f : P* A • Using look-up-table, closed form, etc. • Agent Types: Reflex, state-based, goal-based, utilitybased • Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date DCP 1172, Final 16

Summary - Problem solving and search • Problem formulation usually requires abstracting away real-world Summary - Problem solving and search • Problem formulation usually requires abstracting away real-world details to define a state space that can be explored using computer algorithms. • Uninformed search (depth-first, breadth-first) • Variety of uninformed search strategies; difference lies in method used to pick node that will be further expanded. • Informed search (Heuristic functions, best-first, A*) • Iterative deepening search only uses linear space and not much more time than other uniformed search strategies. • Once problem is formulated in abstract form, complexity analysis helps us picking out best algorithm to solve problem. DCP 1172, Final 17

The Problem Space • Formal way to specify the details of a specific problem; The Problem Space • Formal way to specify the details of a specific problem; captures critical features that influence problem solving • Problem Space: Includes the initial, intermediate and goal states of the problem. • Also includes the problem solver’s knowledge at each of these steps. DCP 1172, Final 18

The Problem Space, Illustrated DCP 1172, Final 19 The Problem Space, Illustrated DCP 1172, Final 19

Planning as a Real-World Problem • What is Planning? • Planning is a search Planning as a Real-World Problem • What is Planning? • Planning is a search problem that requires to find an efficient sequence of actions that transform a system from a given starting state to the goal state • Planning problem has a wide range of applications in the real world • planning in daily life • game world • workflow management DCP 1172, Final 20

Features of Planning Problems • Large search space • Action is associated with system Features of Planning Problems • Large search space • Action is associated with system states • Restrictions on the action sequence • Valid solution may not exist • Optimization requirement DCP 1172, Final 21

Planning - Information Processing Approach • Elements in the Representation of a Problem: • Planning - Information Processing Approach • Elements in the Representation of a Problem: • The givens (or start state) • The goal (or end state) • The operators • The constraints • Example: Tower of Hanoi Problem • Concept of the problem solving process • Heuristics for moving through the problem space • Means-ends analysis DCP 1172, Final 2 22

Problem Space: Operators and Goals • Operators: The set of legal moves that can Problem Space: Operators and Goals • Operators: The set of legal moves that can be performed during problem solving. • Goal: Ultimate solution to the problem. n Well-defined problems explicitly specify the final goal. • Tower of Hanoi -- all disks moved to target peg • Algebra equation -- x = ? ? n Ill-defined problems only vaguely specify the goal state, the operators or both. • Write a coherent essay about………. • Become a millionaire! DCP 1172, Final 23

Algorithms vs. Heuristics • Algorithms • guarantee a solution to a problem • e. Algorithms vs. Heuristics • Algorithms • guarantee a solution to a problem • e. g. , algebra: 3 x + 4 = 2 x • Usually problem specific • Heuristics • don't guarantee a solution to a problem, • cut down search • can be used on a lot of problems DCP 1172, Final 2 94

Heuristics: Means-Ends Analysis • Identify a difference between current state and goal state • Heuristics: Means-Ends Analysis • Identify a difference between current state and goal state • Set a subgoal to reduce the difference. • Apply an operator to reduce the difference • (If operator can’t be applied, new subgoal = remove obstacle that prevents applying the operator) DCP 1172, Final 13 25

Heuristics: Means-Ends Analysis • Unlike search techniques, means-ends analysis can select an action even Heuristics: Means-Ends Analysis • Unlike search techniques, means-ends analysis can select an action even if it is not possible in the current state. • If a planner selects an action that results in the goal state, but is not currently possible, then it will be set as a new goal the conditions necessary for carrying put that actions. • Break the problem down into subgoals and solve these one at a time (E. g. Tower of Hanoi ) • First concentrate on getting the large disk to the third peg DCP 1172, Final 26

Best-first Search Summary • Best-first search = general search, where the minimum-cost nodes (according Best-first Search Summary • Best-first search = general search, where the minimum-cost nodes (according to some measure) are expanded first. • Greedy search = best-first with the estimated cost to reach the goal as a heuristic measure. - Generally faster than uninformed search - not optimal - not complete. • A* search = best-first with measure = path cost so far + estimated path cost to goal. - combines advantages of uniform-cost and greedy searches - complete, optimal and optimally efficient - space complexity still exponential DCP 1172, Final 27

Local Search Summary • We had talked about two very simple approaches • Hill-climbing Local Search Summary • We had talked about two very simple approaches • Hill-climbing • Simulated Annealing • Iterative Improvement • These searches are sometimes referred to as iterative improvement algorithms. • This name stems from the fact that they always have some solution available. • They operate by successively attempting to improve the known solution. DCP 1172, Final 28

Local Search Summary • Time complexity of heuristic algorithms depend on quality of heuristic Local Search Summary • Time complexity of heuristic algorithms depend on quality of heuristic function. • Good heuristics can sometimes be constructed by examining the problem definition or by generalizing from experience with the problem class. • Iterative improvement algorithms keep only a single state in memory. • Can get stuck in local extrema; • simulated annealing provides a way to escape local extrema, and is complete and optimal given a slow enough cooling schedule. DCP 1172, Final 29

Constraint Satisfaction Problem (CSP) • Constraint Satisfaction Problems (or CSP) consists of variables with Constraint Satisfaction Problem (CSP) • Constraint Satisfaction Problems (or CSP) consists of variables with constraints on them. § In CSP problems, states are represented as sets of variables, each with values chosen from some domain § A goal test consists of satisfying constraints on sets of variable/value combinations § A goal state is one that has no constraint violations DCP 1172, Final 30

Constraint Satisfactory Problems (1) • CSP Formulation 1 - incremental formulation as a standard Constraint Satisfactory Problems (1) • CSP Formulation 1 - incremental formulation as a standard search problem : • Initial State: the empty assignment {}; n Start state has no variables assigned • Successor function: n Assign a variable at each step • Goal test: the current assignment is complete. • Apply goal test to completed states • Path Cost: a constant cost (e. g. , 1) for every step • CSP formulation 2 - the complete-state formulation: • Every state is a complete assignment that might or might not satisfy the constraints. • Local search methods work well for this formulation. DCP 1172, Final 31

CSP Heuristics • How do these heuristics differ from the ones that we examined CSP Heuristics • How do these heuristics differ from the ones that we examined in the context of a standard state-space search (for example, in route finding or the 8 -puzzle)? • Hint: domain-specific knowledge • There are two places heuristics can help • Which variable to assign next Degree heuristic • The one involved in the largest number of constraints • Choose the most constrained variable § • Minimum remaining values (MRV) heuristic • Which value to assign to a variable • Choose as a value the one that leaves the most choices remaining for other variables • Least-constraining value heuristic DCP 1172, Final 32

Why Study Games ? (1) • Game playing was one the first tasks undertaken Why Study Games ? (1) • Game playing was one the first tasks undertaken in AI. • By 1950, Chess had been studied by many forerunners in AI ( e. g. , Claude Shannon, Alan Turing, etc. ) • For AI researchers, the abstract nature of games make them an appealing feature for study. • The state of a game is easy to represent, • and agents are usually restricted to a small number of actions, • whose outcomes are defined by precise rules. DCP 1172, Final 33

Two-player games • A game formulated as a search problem: • • Initial state: Two-player games • A game formulated as a search problem: • • Initial state: board position and turn Successor functions: definition of legal moves Terminal state: conditions for when game is over Utility function: a numeric value that describes the outcome of the game. E. g. , -1, 0, 1 for loss, draw, win. (AKA payoff function) DCP 1172, Final 34

Why Study Games ? (2) • Games are interesting because they are too hard Why Study Games ? (2) • Games are interesting because they are too hard to solve. • Games requires the ability to make some decision even when calculating the optimal decision is infeasible. • Games also penalize inefficiency severely. • Game-playing research has therefore spawned a number of interesting ideas on how to make the best possible use of time. DCP 1172, Final 35

Summary – Game Playing DCP 1172, Final 36 Summary – Game Playing DCP 1172, Final 36

Knowledge-Based Agents § A knowledge-based agent is composed of a knowledge base and an Knowledge-Based Agents § A knowledge-based agent is composed of a knowledge base and an inference mechanism. • A knowledge-base is simply a repository of domainspecific things (or sentences about the world) that you know represented in some useful way. § A knowledge-based agent operates by storing sentences about the world in its knowledge base, using the inference mechanism to infer new sentences, and using these sentences to decide what action to take. • The knowledge base cannot be a simple table because an agent should be able to conclude facts about the world that are not already represented in the knowledge base. DCP 1172, Final 37

Semantic networks Linguists noticed long ago that the structure of a sentence can be Semantic networks Linguists noticed long ago that the structure of a sentence can be represented as a network. • Words of the sentence are nodes, and they are bound by arcs expressing relations between the words. • The network as a whole represents in this way a meaning of the sentence in terms of meanings of words and relations between the words. • This meaning is an approximation of the meaning people can assign to the sentence, analogous in a way to other approximate representations of the meaning, for instance, how floating point numbers represent the approximate meaning of real numbers. • DCP 1172, Final 38

Example John must pick up his report in the morning and have a meeting Example John must pick up his report in the morning and have a meeting after lunch. After the meeting he will give the report to me. DCP 1172, Final 39

Example continued Inferences can be made, depending on the properties of the relations of Example continued Inferences can be made, depending on the properties of the relations of a semantic network. Let us consider only time relations of the network in our example, and encode the time relations by atomic formulas as follows: before(lunch, morning) after(morning, lunch) = general knowledge after(lunch, have a meeting) after(have a meeting, give) at-the-time(morning, pick up) = specific knowledge DCP 1172, Final 40

Example continued Inference rules: before(x, y) before(y, z) before(x, z) after(x, y) before(y, x) Example continued Inference rules: before(x, y) before(y, z) before(x, z) after(x, y) before(y, x) at-the-time(x, z) before(y, x) Applying these rules, we can infere after(lunch, have a meeting) before(have a meeting, lunch) at-the-time(pick up, morning) before(lunch, pick up) and before(lunch, morning) DCP 1172, Final etc. 41

Rules are a well-known form of knowledge which is easy to use. A rule Rules are a well-known form of knowledge which is easy to use. A rule is a pair (condition, action) which has the meaning: "If the condition is satisfied, then the action can be taken. " Also other modalities for performing the action are possible - "must be taken", for instance. DCP 1172, Final 42

Using rules • Let us have a set of rules called rules and functions Using rules • Let us have a set of rules called rules and functions cond(p) and act(p) which select the condition part and action part of a given rule p and present them in the executable form. • The following is a simple algorithm for problem solving with rules: while not good do found : = false; for p Î rules do if cond(p) then act(p); found: =true fi od; if not found then failure fi od DCP 1172, Final 43

Decision trees A simple way to represent rules is decision tree: • a tree Decision trees A simple way to represent rules is decision tree: • a tree with nodes for attributes and arcs for attribute values. Example: legs two four hands furry no yes furry yes monkey no bird table yes animal no man DCP 1172, Final 44

Frames 1. The essence of the frame is that it is a module of Frames 1. The essence of the frame is that it is a module of knowledge about something which we can call a concept. This can be a situation, an object, a phenomenon, a relation. 2. Frames contain smaller pieces of knowledge: components, attributes, actions which can be (or must be) taken when conditions for taking an action occur. 3. Frames contain slots which are places to put pieces of knowledge in. These pieces may be just concrete values of attributes, more complicated objects, or even other frames. A slot is being filled in when a frame is applied to represent a particular situation, object or phenomenon. DCP 1172, Final 45

Inheritance An essential idea developed in connection with frames was inheritance. Inheritance is a Inheritance An essential idea developed in connection with frames was inheritance. Inheritance is a convenient way of reusing existing knowledge in describing new frames. Knowing a frame f, one can describe a new frame as a kind of f, meaning that the new frame inherits the properties of f, i. e. it will have these properties in addition to newly described properties described. Inheritance relation expresses very precisely the relation between super- and sub-concepts. DCP 1172, Final 46