Intelligent System Project

Transcription

1 Intelligent System Project (Part VI INTELLIGENT METHODS & MODELS) Miquel Sànchez i Marrè miquel@cs.upc.edu Course 2018/2019

2 PART 6 INTELLIGENT METHODS & MODELS

3 PRINCIPLES OF ARTIFICIAL INTELLIGENCE AI Paradigms

4 AI Paradigms or Approaches The deliberative/symbolic paradigms Concerned on the processing of symbols rather than numerical values. Use a latent reasoning mechanism. Most of them are Cognitive-inspired approaches. The reactive/subsymbolic paradigms Concerned about more numerical computations and providing nice and intelligent optimizations schemes or function approximation schemes. No evident reasoning mechanisms are used. Most of them are Bio-inspired approaches. 4

5 Deliberative Approaches (1) Logic paradigm: based on representing the knowledge about the problem and the domain theory through logical formulas. The main reasoning mechanism is the automatic theorem proving using the automated resolution process set by Robinson. It is a very general mechanism. Major techniques are based on Logic Programming. man(john). man(peter). man(michael). woman(ann). woman(eliza). woman(ada). father(michael, peter). father(john, michael). mother(ann, peter). Miquel Sànchez i Marrè, KEMLG,

6 Deliberative Approaches (2) grandfather(x,y) :- man(x), father(x,z), father(z,y). grandfather(x,y) :- man(x), father(x,z), mother(z,y). grandmother(x,y) :- woman(x), mother(x,z), father(z,y). grandmother(x,y) :- woman(x), mother(x,z), mother(z,y). son(x,y) :- man(x), (father(y,x); mother(y,x)). daughter(x,y) :- woman(x), (father(y,x); mother(y,x)). brother(x,y) :- man(x), ((mother(z,x), mother(z,y); (father(z,x), father(z,y))). sister(x,y) :- woman(x), ((mother(z,x), mother(z,y); (father(z,x), father(z,y))). Using the Prolog interpreter:? grandfather(john,x) Yes X= peter Miquel Sànchez i Marrè, KEMLG,

7 Deliberative Approaches (3) Heuristic search and planning paradigm: it is based in searching within a space of possible states, starting from the initial state to a final state, where the problem has been solved. The state space is a graph structure to be intelligently explored. A state is the representation of the current state of problem solving. For instance, the initial state (s 0 ) of a numerical eightpuzzle can be the following one: Miquel Sànchez i Marrè, KEMLG,

8 Deliberative Approaches (4) Heuristic search and planning paradigm: it is based in searching within a space of possible states, starting from the initial state to a final state, where the problem has been solved. The state space is a graph structure to be intelligently explored. Most commonly used techniques are the A* algorithm and other heuristic search approaches. S 0 f = g + h S 1 S 2 S 3 S S 5 S F S F S F S F Miquel Sànchez i Marrè, KEMLG,

9 Deliberative Approaches (5) Knowledge-Based paradigm: this kind of approach tries to get benefit from the particular knowledge of a concrete domain, which is normally used by experts when facing the problems to be solved. This knowledge is encoded in what has been named as a Knowledge Base. Most common knowledge bases are implemented as inference rules (IF <conditions> THEN <actions>). The knowledge is explicitly represented by the inference rules. Main examples of this paradigm are the Expert Systems and the Intelligent Tutoring Systems. The reasoning mechanisms are the forward and backward reasoning engines. The technique used here is commonly known as Rule-Based Reasoning (RBR), or sometimes also known as Knowledge- Based System (KBS). Miquel Sànchez i Marrè, KEMLG,

10 A Rule-based Reasoning system (expert system) to diagnose whether a customer can be awarded with a loan or not for launching a new software company for developing IDSSs (1) The Fact Base would be formed by: Assets-value (AV) Amount-required (N) Financial-support (FS) Financial-history (FH) Amount-already-pending (AAP) Reliability-of-devolution (RD) Company-Viability (CV) Loan-given (I) Loan-not-given () Loan-given-with-preferential-interest (PI) Miquel Sànchez i Marrè, KEMLG,

11 A Rule-based Reasoning system (expert system) to diagnose whether a customer can be awarded with a loan or not for launching a new software company for developing IDSSs (2) Rule Base would be formed by: Assets-value < 5*10 5 Insufficient-assets-value Assets-value 5*10 5 Assets-value < 3*10 6 Sufficient-assets-value Assets-value 3*10 6 Excellent-assets-value... Financial-support=low Insufficient-assets-value Loan-not-given Reliability-of-devolution=low Loan-not-given... Financial-support=good Sufficient-assets-value Financial-history=good Company-Viability=good Loan-given (I=3)... Financial-support=high Excellent-assets-value Financial-history=good Company-Viability=very-good Loan-given (I=2.25)... Miquel Sànchez i Marrè, KEMLG,

12 Deliberative Approaches (6) Model-Based paradigm: this approach is very similar to the Knowledge-based, because the knowledge of a particular domain is used. The difference relies on the fact that the knowledge is implicitly encoded in some kind of model. Most common approaches are causal models reflecting the causal relationships among several components of a system, or qualitative models reflecting the qualitative relationships among several attributes which are characterizing the domain. The reasoning process is done through some kind of interpreter of the model and its component relationships. Model-Based Reasoning (MBR) and Qualitative Reasoning (QR) are major techniques using this approach Miquel Sànchez i Marrè, KEMLG,

13 Qualitative Reasoning Natural System Miquel Sànchez i Marrè, KEMLG, 2015

14 Component-oriented Model Motorbike Part-of Part-of Part-of Ignition System Carburetion System... Transmission System Part-of Part-of Part-of Spark Plugs Spark Part-of Spun Miquel Sànchez i Marrè, KEMLG, 2013 Ignition Circuit Part-of Electrodes

15 Deliberative Approaches (7) Experience Based paradigm: this approach tries to solve new problems in a domain using the solution given in the past to a similar problem in the same domain (analogical reasoning). Thus, the solved problems constitute the knowledge about the domain. As more experienced is the system better performance achieves, because the experiences (cases or solved problems) are stored in the Case Base. This way the system is continuously learning to solve new problems. The technique used in this approach is known as Case-Based Reasoning (CBR) or Instance-Based Reasoning. Miquel Sànchez i Marrè, KEMLG,

16 Case-Based Reasoning CBR Cycle: new case Retrieve retrieved cases case to store Learn CASE LIBRARY best case Adapt Miquel Sànchez i Marrè, KEMLG, 2014 DOMAIN KNOWLEDGE adapted solution evaluated solution (fail/ success) Eval 16

17 Reactive Approaches (1) Connectionism paradigm: this approach is inspired by the biological neural networks which are in the brain of many living beings. The model of an Artificial Neural Network (ANN) mimics the biological neural networks with the interconnection of artificial neurons. The ANN will produce an output result, as an answer to several input information from the input layer neurons, emulating the neural networks of the brain which propagate signals among all the neurons interconnected. ANNs are general approximation functions very useful in nonlinear conditions Miquel Sànchez i Marrè, KEMLG,

18 Artificial Neural Networks (ANN) A simple model of one artificial neuron w ij : weights x 0 = 1 f: Activation function x 1 w 1,1 w 0,1 =b output x 2 w 2,1... Miquel Sànchez i Marrè, KEMLG, 2014 x w n n,1 net Σ f inputs artificial neuron 1 o net: network agregation o = f(net) = f(wx+b)

19 Reactive Approaches (2) Evolutionary Computation paradigm: Evolutionary computation is a bio-inspired approach mimicking selection natural process in biological populations. It uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search being able to use parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. Evolutionary computation provides a biological combinatorial optimization approach. Miquel Sànchez i Marrè, KEMLG,

20 Genetic Algorithms Solves a problem by evolving its solution incrementally Population at generation N Have three basic operators: selection, crossover and mutation New population generation Evaluating fitness Works on encoded individuals Representation of solutions in the form of encoded individuals is the primary step Mutation Crossover Selection 20

21 Reactive Approaches (3) Uncertainty reasoning paradigms: A Bayesian network, belief network or directed acyclic graphical model Miquel Sànchez i Marrè, KEMLG, 2015 Is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). They provide an inference reasoning mechanism to obtain the new probability values of any variable within the network after some new evidences are known 21

22 Bayesian Networks Season {Summer, Winter, Spring, Fall} Rain {Yes, No} X2 Wet Floor {Yes, No} Slippery {Yes, No} X1 X4 X5 Sprinkler {Yes, No} X3 Miquel Sànchez i Marrè, KEMLG, 2015 Qualitative part Structure: Aciclic directed graphs nodes: domain variables Quantitative part: Association strength: conditional probability distribucions between parents and children Operations Consult (propagation): how the probability distributions of other variables change as a certain value has been observed Explanation (abducció): provide the set of variables and assignments more plausible to explain the value of a certain variable Advantage They permit to manage very complex joint probability distributions concerning large number of variables, by means of conditional probabilities

23 Reactive Approaches (4) Uncertainty reasoning paradigms: Fuzzy logic systems Systems based on fuzzy logic and possibilistic theory to model the vagueness and imprecision concepts. The mathematical possibilistic model assigns a possibility value to each element which is evaluated regarding whether it belongs to a set. Values are evaluated in terms of logical variables that take on continuous values between 0 and 1. Cool Temperature ºC Miquel Sànchez i Marrè, KEMLG, Young Age Years Sharon 23

24 Reactive Approaches (5) Other optimization paradigms: there are several optimization techniques approaches ranging from Collective problem solving techniques named as Swarm Intelligence such as Ant colony optimization Swarm particle optimization Etc. Until another simpler optimization techniques using several meta-heuristics Tabu search Simulated Annealing Miquel Sànchez i Marrè, KEMLG,

25 Data Mining / Data Science / Data Analysis Extract new, useful knowledge from data Methods / Models: Statistics Machine Learning/Artificial Intelligence Hybrid Miquel Sànchez i Marrè, KEMLG,

26 Model Clasification (1) Data Mining Models Models with No response variable / Unsupervised models Models with response variable / Supervised models Descriptive Models Associative Models Discriminant Models Predictive Models (IA) Conceptual Clustering Self Organising Maps (SOMs) (Stats) Statistical clustering (IA&Stats) Clustering based on rules (ClBR) (IA) Association Rules Model-based Reasoning Qualitative Reasoning (Stats) Principal Component Analysis (PCA) Simple Correspondence Analysis (SCA) Multiple Correspondence Analysis (MCA) (IA&Stats) Bayesian networks (BayNet) Miquel Sànchez i Marrè & Karina Gibert, KEMLG, 2015 Case-based models (IA) Case-based Classifier (CBRClas) Rule-based models (IA) Rule-based Classifiers Decision Trees (Stats) Linear Discriminant Analysis (LDA) Logistic/Multinomial/ /Ordinal Regression (IA&Stats) Boxplot-based Induction Rules Regression Trees Model Trees Support Vector Machines (SVM) Bayesian models (IA&Stats) Naïve Bayes Classifier (IA) Connexionist models (NeuralNet) Case-based Predictor (CBRPred) Evolutionary Computing (GAs) Swarm Intelligence (Stats) Linear Regression (LR) Multiple Linear Regression (MLR) Analysis of Variance (ANOVA) Generalized Linear Models (GLM) Time Series (TS) 26

27 Model Classification (2) UNCERTAINTY MODELS PROBABILISTIC MODELS NEAR-PROBABILISTIC MODELS EVIDENTIAL MODEL POSSIBILISTIC MODEL (Stats) Pure Probabilistic Model (AI&Stats) Bayesian Network Model [Pearl] Miquel Sànchez i Marrè, KEMLG, 2015 (AI) Certainty Factor Method [MYCIN] (AI) Subjective Bayesian Method [PROSPECTOR] (AI) Evidence Theory [Dempster-Shafer] (AI) Possibility Theory Fuzzy Logic [Zadeh] 27

28 Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Miquel Sànchez-Marrè 28