V. Ashwini Kumar Structure of Teaching-Learning Process January 02, 2017

Learning Level

(Adaptation from Bloom’s

Taxonomy)

Input (No. of hours/week)

[Teacher and Student Deliverables]

Area of Focus of Learning

Nature of Work

Total No. of

hours/week of Work

Outcome as an Action that a Student is

Capable of

1 Remember Understand

Lecture (1 hour/week) (delivered by teacher)

[handouts covering ½ Unit in each Lecture of 1 hour, Define and explain using an example]

Understanding Idea/Concept

Theory (LECTURE)

4

Define, Mention, Describe, Explain, Refer, Infer, Relate, Example

2 Apply

Tutorial (2 hours/week) (delivered by student under teacher’s supervision) [5 Problems to be solved and solutions discussed in each

tutorial of 2 hours; a total of 10 problems per Unit, and 50 problems covering the syllabus and an equal number of problems to be solved by student making the total 100]

Idea/Concept Illustration and

Solution Processes

Select, Compare, Decide, Solve, Prove, Illustrate, Use, Apply

3 Analyze

Assignment (1 hour/week) (delivered by student under teacher’s instructions)

[Total 5 Case Studies; Assignment 1: 3 Case Studies; Assignment 2: 2 Case Studies.

Workout each Case Study in 1 hour and present salient points of Analysis of Case Study in the next week 1 hour for

immediate evaluation]

Solution Processes in Case-Studies

Refer, Infer, Relate, Compare, Distinguish, Prove, Measure, Analyze, Evaluate, Design, Test, Case Study (a simplified version of real life case),

Report

4 Evaluate

Experimental-Work (1 hour/week) (prepared by teacher and executed by student under

supervision of teacher) [Total 3 Experiments in a Semester; perform one experiment under supervision in 1 hour and explore independently in 1

hour of the next week, followed by a presentation of the report and results for immediate evaluation in 1 hour of the

third week]

Study of Hypothesis in

Controlled Environment and

Reporting

Practical (EXPERIMENTS

and MINI-

PROJECT)

Beyond Class Work

3

Hypothesize, Formulate, Design, Explore, Test, Observe, Analyze,

Evaluate, Discuss, Infer, Conclude, (Experiment) Report

5 Create

Mini-Project (2 hours/week) (prepared, executed, developed, reported and

presented by 3 students with support of idea and direction for solution from teacher)

[1 Mini-Project in a semester of 16 weeks with 12 weeks availability; 2 hours/week for 10 weeks, followed by 2 hour presentation by 3

students along with teacher’s feedback and 2 hour evaluation (total 12 weeks) A teacher will have at least 5 mini project ideas from the

subjects taught, kept ready at the beginning of a Semester]

Real-life Application

Development and Reporting

Explore, Search, Refer, Formulate, Analyze, Solve, Prove, Design, Test, Evaluate, Discuss, Develop, Infer, Conclude, (Mini-Project) Report

BIT 354 ARTIFICIAL INTELLIGENCE w.e.f 2013-2014

Instruction: 4 Periods per Week; Duration of University Examination: 3 Hours; University Examination: 75 Marks; Sessional: 25 Marks

Unit – I Introduction. History. Intelligent Systems. Foundations of AI. Subareas of AI Applications. Problem Solving – State-Space Search and Control Strategies: Introduction. General Problem Solving. Characteristics of Problem. Exhaustive Searches. Heuristic Search Techniques. Iterative-Deepening A*. Constraint Satisfaction. Game Playing. Bounded Look-Ahead Strategy and use of Evaluation Functions. Alpha-Beta Pruning. Unit – II Logic Concepts and Logic Programming: Introduction. Propositional Calculus. Propositional Logic. Natural Deduction System. Axiomatic System. Semantic Tableau System in Propositional Logic. Resolution Refutation in Propositional Logic. Predicate Logic. Logic Programming. Knowledge Representation: Introduction. Approaches to Knowledge Representation. Knowledge Representation using Semantic Network. Extended Semantic Network for KR. Knowledge Representation using Frames. Unit – III Expert System and Applications: Introduction. Phases in Building Expert Systems. Expert System Architecture. Expert System vs Traditional Systems. Truth Maintenance Systems. Application of Expert Systems. List of Shells and Tools. Uncertainty Measure – Probability Theory: Introduction. Probability Theory. Bayesian Belief Networks. Certainty Factor Theory. Dempster-Shafer Theory. Unit – IV Machine-Learning Paradigms: Introduction. Machine Learning Systems. Supervised and Unsupervised Learning. Inductive Learning. Learning Decision Trees (Suggested Reading 2). Deductive Learning. Clustering. Support Vector Machines. Artificial Neural Networks: Introduction. Artificial Neural Networks. Single-Layer Feed-Forward Networks. Multi-Layer Feed-Forward Networks. Radial-Basis Function Networks. Design Issues of Artificial Neural Networks, Recurrent Networks. Unit – V Advanced Knowledge Representation Techniques: Case Grammars, Semantic Web. Natural Language Processing: Introduction, Sentence Analysis Phases. Grammars and Parsers. Types of Parsers. Semantic Analysis. Universal Networking Knowledge. Suggested Reading:

1. Saroj Kaushik, Artificail Intelligence. Cengage Learning, 2011. 2. Russel, Norvig, Artificial Intelligence, A Modern Approach, Person Education, Second Edition, 2004. 3. Rich, Knight, Nair: Artificial Intelligence, Tata McGraw Hill, Third Edition, 2009.

Course: Artificial Intelligence BIT 354 Objectives: To

1. Define and explain Problem Solving and Game Playing exploring Search-Methods to evaluate and apply Search Heuristics using A*,

Constraint Satisfaction, and Iterative Deepening for illustration.

2. Relate Logic concepts from Propositional and Predicate Logic, and discuss Resolution Refutation, along with Semantic Nets and Frames

for knowledge representation and reasoning.

3. Analyze phases of development of an Expert System and Rule-Base. Establish a theoretical framework to handle uncertainty through

Bayes Belief Networks, Certainty Factor theory, and Dempster-Shafer theory.

4. Distinguish Supervised and Unsupervised Learning, and perform Inductive and Deductive Learning using Decision Trees, Clustering, and

Support Vector Machines. Distinguish Feedforward and Recurrent Neural Networks. Use Backpropagation and Radial Basis Function to

train networks for use.

5. Analyze, Design and Develop Case and Semantic Grammars, Augmented Transition Networks, and Web Ontology. Create Words in

Universal Networking Language.

Outcomes: Student will be able to

1. Formulate and demonstrate exhaustive and heuristic search methods in solving problems using A*, Constraint Satisfaction, and iterative

Deepening.

2. Formulate and describe real world knowledge in terms of Propositional Logic, Predicate Logic, Semantic Nets, and Frames and

demonstrate reasoning using the same.

3. Design a simple Expert System Rule Base using knowledge base and construct Bayes Belief Network for real world problem to

demonstrate causal and diagnostic reasoning, and distinguish between uncertainty and ignorance.

4. Use Decision Tree, Clustering, and Support Vector Machine Algorithms to solve and demonstrate inductive and deductive learning.

Demonstrate Learning in Feedforward Neural Networks using Backpropagation and distinguish between Feedforward and Recurrent

Neural Networks.

5. Design and Analyze Case and Semantic Grammars. Develop Augmented Transition Networks and Ontology. Demonstrate addition of

words to Universal Networking Language to make machine translation of a natural language to many other natural languages.

V. Ashwini Kumar Objectives and Outcomes January 02, 2017

Lecture Handout: 2 Handouts are given covering one Unit in a lecture of 2 hours at the beginning of each Unit. A total of 10 Handouts are given to student. This includes reference to Books, Open Sources, Professional Societies, Conferences, Journals, Current Research and Breakthroughs. 1.1. Introduction to AI 1.2. Problem Solving and Game Playing 2.1. Logic Concepts & Programming 2.2. Knowledge Representation 3.1. Expert Systems and Applications 3.2. Uncertainty – Bayesian Belief Networks, Certainty Factor, Dempster-Shafer Theory

4.1. Machine Learning 4.2. Artificial Neural Networks 5.1. Advanced Knowledge Representation 5.2. Natural Language Processing

Tutorials: Concept Oriented Problems & Solution Processes Illustration. Student should solve 10 problems from each Unit, and 50 problems covering all 5 Units, under Teacher’s supervision. Student should solve 50 additional exercise problems, independently at home. The 50 Tutorial Problems are: 1.1. State Space Representation – Tic-Tac-Toe 1.2. State Space Representation – Water Jug Problem 1.3. Exhaustive Search and Complexity 1.4. Heuristic Search – Eight Puzzle Problem 1.5. Search Efficiency 1.6. Iterative Deepening 1.7. A* 1.8. Constraint Satisfaction 1.9. Alpha-Beta Pruning 1.10. Travelling Salesman Problem 2.1. Propositional Logic 2.2. Natural Deduction System 2.3. Axiomatic System 2.4. Semantic Tableau System 2.5. Resolution Refutation in Propositional Logic 2.6. Predicate Logic 2.7. Logic Programming 2.8. Semantic Nets 2.9. Extended Semantic Net 2.10. Frames 3.1. Traditional Vs Expert System 3.2. Truth Maintenance Systems 3.3. Development of Expert System 3.4. Tools for Development of Expert System 3.5. Probability Theory and Chain Rule 3.6. Bayes Theorem and Conditional Probability

3.7. Conditional Independence and Joint Probability 3.8. Bayes Belief Network development 3.9. Bayes Belief/Causal Network and Causal Reasoning (Top-down Inference) 3.10. Bayes Belief/Causal Network and Diagnostic Reasoning (Bottom-up Inference)4.1. Supervised Learning 4.2. Unsupervised Learning 4.3. Inductive Learning – Decision Trees 4.4. Deductive Learning – k-Means Clustering 4.5. Deductive Learning – Support Vector Machine 4.6. Perceptron 4.7. Single Layer Feedforward Network 4.8. Multilayer Feedforward Network-Backpropagation 4.9. Multilayer Feedforward Network-Radial Basis Function 4.10. Recurrent Network 5.1. Case Grammars 5.2. Resource Description Framework RDF 5.3. Resource Description Framework Schema RDF Schema 5.4. Ontology (protégé.stanford.edu) Development 5.5. Web Ontology Language (OWL) 5.6. Sentence Analysis 5.7. Link and Chart Parsers 5.8. Transition Networks, Recursive Transition Networks 5.9. Augmented Transition Networks 5.10. Universal Networking Language – use grammar to define words of the Language

V. Ashwini Kumar List of Handouts and Tutorial Problems January 02, 2017

Assignments:

Solution Processes in Case Studies. Case Studies are simplified versions of real-life problems.

1. Case Study 1: Multivariable Optimization – A Heuristic for Search in the right direction. Analyze, Design, Test, Evaluate, Report

2. Case Study 2: Multivariable Interpolation – A special category of Feedforward ANN inspired by functioning of cerebral cortex: Radial

Basis Function Network (RBFN). Study M. Powell’s work (1985). Refer, Relate, Evaluate, Report

3. Case Study 3: Classification – Support Vector Machine (SVM). Classify real-life tuples (n dimensional data) by searching for linear

separating maximum margin hyperplane in projected (n+1) dimensions. Prove, Test, Report (www.csie.ntu.edu.tw/~cjlin/talks/MLSS.pdf)

4. Case Study 4: Multilayer Feedforward ANN with Backpropagation – Lean to predict the output of a specified simplified real-life system.

Refer, Analyze, Design, Test, Evaluate, Report

5. Case Study 5: Resource Description Framework RDF and its Schema (RDF Schema) – a Machine Interpretable Definition of Concepts.

Prepare RDF Schema for a Student – Course – Curriculum – Outcomes Case and discuss the use of Schema. Distinguish, Discuss, Use.

V. Ashwini Kumar Assignments January 02, 2017

Experimental-Work:

Study the Hypothesis in Controlled Environment and Report the Experiment.

Experiment 1: To show that it is (always (for all observations)) possible to predict a correct yes/no as output given an object or

situation described by a set of properties as inputs in the form of a Decision Tree. (Tom Mitchell)

Experiment 2: Perform an experiment to show that it is (always) possible to generate clusters using k-Means Clustering on the given

data provided that the data satisfies a set of Specified Criteria.

Experiment 3: To show that it is possible to construct a Rule-Base using facts (knowledge-base) by applying Predicate Logic

Experiment 4: To show that it is possible to construct a Rule-Base using facts (knowledge-base) by applying Fuzzy Sets with Fuzzy

Logic. (Zadeh)

Experiment 5: To show that it is possible to construct a Rule-Base using facts (knowledge-base) by applying Rough Sets with Rough

Logic. (Pawlak. Z and Tom Mitchell))

V. Ashwini Kumar Experiments January 02, 2017

Mini-Projects: Search, Design, Develop, Analyze, Discuss, Conclude, Report

1. Problem Solving using a partial State Space Search by development of a recursive relation between the number of moves, Hn and the

number of disks, n in Towers-of-Hanoi Problem that gives the least number of moves for a given number of disks.

An Exploration for Development of Least-Moves Recursive-Solution to Towers-of-Hanoi using State-Space-Search

2. Problem Solving using Heuristic Function and A* Algorithm in State Space Search for Eight-Puzzle-Problem.

Heuristic Function Evaluation and its Use for Efficient State-Space-Search for Solution of Eight-Puzzle-Problem

3. Reasoning using Bayes Belief Network for a Robot System with Conditional-Independence and Joint-Probability.

Development of a Bayes Belief Network for a Robot using Random Variables with Joint-Probability for Causal Reasoning

4. Decision Tree Induction (Attribute Selection Measures: Information Gain, Gain Ratio, and Gini Index)

A Comparative Study for Development of Decision Tree for Enrolling for a Course using Various Attribute Selection Measures

5. Clustering using 2-D real life data.

A Practical and Critical Analysis to Discover Limitations of k-Means Clustering Algorithm to Cluster Data in 2-D Space

Anti Plagiarism Software: www.ithenticate.com/products/crosscheck; www.crossref.org/crosscheck

V. Ashwini Kumar Mini-Projects January 02, 2017

Text Books: (Prescribed) 1. Saroj Kaushik, Artificial Intelligence, Cengage Learning 2011. 2. Russel, Norvig, Artificial Intelligence, A Modern Approach, Pearson Education, 2004. 3. Rich, Knight, Nair, Artificial Intelligence, Tata McGraw-Hill, Third Edition, 2009.

Additional Reading:

Open Source References: 1. protégé.stanford.edu ONTOLOGY DEVELOPMENT 2. www.undl.org UNIVERSAL NETWORKING LANGUAGE 3. wc3.org/standards/techs OWL (Web Ontology Language) 4. clipsrules.sourceforge.net CLIPS NASA (Expert System) 5. logtalk.org LOGTALK (object oriented PROLOG extension logic programming) 6. xsb.sourceforge.net (logic programming and deductive database system) 7. bnj.sourceforge.net (Bayesian network tools in java)

Societies: 1. Special Interest Group on Artificial Intelligence, Computer Society of India 2. IEEE Computer Society, IEEE Computational Intelligence Society, IEEE Robotics and Automation Society 3. Association for Computing Machinery, Special Interest Group in Artificial Intelligence (ACM SIGART) 4. Association for the Advancement of Artificial Intelligence http://www.aaai.org 5. Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) 6. The European Association for Artificial Intelligence EurAI (formerly ECCAI) 7. CAIAC is the Canadian Artificial Intelligence Association 8. The Swedish Artificial Intelligence Society, SAIS http://www.sais.se/ 9. The Japanese Society for Artificial Intelligence (JSAI)

V. Ashwini Kumar Books, References and Societies January 02, 2017

Conferences Publications Field Rating

AAAI - National Conference on Artificial Intelligence 5399 132

IJCAI - International Joint Conference on Artificial Intelligence 5324 130

ICML - International Conference on Machine Learning 2457 127

ICRA - International Conference on Robotics and Automation 17209 110

ICGA - International Conference on Genetic Algorithms 545 86

AAMAS(Agents) - Autonomous Agents & Multiagent Systems/International Conference on Autonomous Agents 1664 82

UAI - Uncertainty in Artificial Intelligence 1573 82

KR - Principles of Knowledge Representation and Reasoning 774 72

IROS - International Conference on Intelligent RObots and Systems - IROS 11959 70

CEC - IEEE Congress on Evolutionary Computation 4023 62

ECAI - European Conference on Artificial Intelligence 2584 56

PPSN - Parallel Problem Solving from Nature 1068 56

GECCO - Genetic and Evolutionary Computation Conference 4235 55

JSAI Workshops 552 54

International Conference on Evolutionary Computation 640 50

ICMAS - International Conference on Multiagent Systems 353 50

ATAL - Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages 1969 49

EC - ACM Conference on Electronic Commerce 477 48

SMC - IEEE International Conference on Systems, Man, and Cybernetics 15299 47

ISMIR - International Symposium/Conference on Music Information Retrieval 774 47

AIED - Artificial Intelligence in Education 799 44

IEEE International Conference on Fuzzy Systems 6461 42

ICAPS(AIPS) - International Conference on Automated Planning and Scheduling/Artificial Intelligence Planning Systems 623 42

SAB - Simulation of Adaptive Behavior 361 39

ECAL - European Conference on Artificial Life 666 38

Machine Intelligence 164 38

Foundations of Genetic Algorithms 209 35

ILP - International Workshop on Inductive Logic Programming 449 34

DLog - Description Logics 619 33

LPNMR - Logic Programming and Non-monotonic Reasoning 450 33

ICCBR - Case-Based Reasoning and Development 499 32

JELIA - Logics in Artificial Intelligence 428 32

Evolutionary Multi-Criterion Optimization 330 32

TARK - Theoretical Aspects of Rationality and Knowledge 299 32

ICAIL - International Conference on Artificial Intelligence and Law 489 31

Evolutionary Programming 235 31

ISER - International Symposium on Experimental Robotics 597 30

EWCBR - European Workshop on Case-Based Reasoning 456 30

EKAW - Knowledge Acquisition, Modeling and Management 420 30

CDC - Conference on Decision and Control 331 30

PATAT - Practice and Theory of Automated Timetabling 168 30

DAI - Distributed Artificial Intelligence 118 30

KI - German Conference on Artificial Intelligence 1230 29

ICCS - International Conference on Conceptual Structures 633 29

Ant Algorithms 619 29

ICES - International Conference on Evolvable Systems 338 29

RoboCup - RoboCup International Symposium 1003 28

Theory and Applications of Satisfiability Testing 348 28

AOSE - Agent-Oriented Software Engineering 178 28

EvoWorkshops 1181 26

ISMIS - International Syposium on Methodologies for Intelligent Systems 1055 26

HRI - Human-Robot Interaction 528 26

EH - Evolvable Hardware 286 26

ICARIS - International Conference on Artificial Immune Systems 270 26

K-CAP - International Conference on Knowledge Capture 229 26

MAAMAW - Modelling Autonomous Agents in a Multi-Agent World 127 26

ECP - European Conference an Planning 84 26

FLAIRS - The Florida AI Research Society Conference 1584 25

CAIA - Conference on Artificial Intelligence Applications 616 25

EuroGP - European Conference on Genetic Programming 447 25

TABLEAUX - Analytic Tableaux and Related Methods 324 25

RSS - Robotics: Science and Systems 180 25

FOIS - International Conference on Formal Ontology in Information Systems 166 25

IROS - International Conference on Intelligent Robots and Systems 152 23

IEA/AIE - Industrial and Engineering Applications of Artificial Intelligence and Expert Systems 2366 22

RSCTC - Rough Sets and Current Trends in Computing 650 22

ECSQARU - Symbolic and Quantitative Approaches to Reasoning and Uncertainty 605 22

Intelligent Autonomous Systems 400 22

ISRR - International Symposium of Robotics Research 136 22

Distributed Autonomous Robotic Systems 120 22

AUS-AI - Australian Joint Conference on Artificial Intelligence 1244 21

AIME - AI in Medicine in Europe 482 21

Artificial Intelligence and the Simulation of Behaviour 136 21

ETFA - Emerging Technologies and Factory Automation 2971 20

IDA - Intelligent Data Analysis 430 20

CONTEXT - Conference on Modeling and Using Context 307 20

CIA - Cooperative Information Agents 288 20

AE - Artificial Evolution 215 20

ESAW - Engineering Societies in the Agent World 212 20

AMEC - Agent-Mediated Electronic Commerce 83 20

ECAI(Workshop) - European Conference on Artificial Intelligence 65 20

KES - Knowledge-Based Intelligent Information & Engineering Systems 3834 19

IC-AI - International Conference on Artificial Intelligence 1620 19

PRICAI - Pacific Rim International Conference on Artificial Intelligence 962 19

CIRA - Computational Intelligence in Robotics 791 19

Canadian Conference on Artificial Intelligence 623 19

Intelligent Virtual Agents 506 19

Spatial Cognition 196 19

ICTAI - International Conference on Tools with Artificial Intelligence 1379 18

IAT - International Agent Technology Conference 1060 18

EPIA - Portuguese Conference on Artificial Intelligence 542 18

TIME - Workshops 258 18

RecSys - Conference on Recommender Systems 258 18

FSR - Field and Service Robotics 176 18

RWEB - Reasoning Web 72 18

Issues in Agent Communication 45 18

RSFDGrC - Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing 484 17

NMR - Non-Monotonic Reasoning 125 17

Collective Robots 63 17

Journals and Rankings

Title Type SJR

H

index

Total

Docs.

(2014)

Total

Docs.

(3years)

Total

Refs.

Total

Cites

(3years)

Citable

Docs.

(3years)

Cites /

Doc.

(2years)

Ref. /

Doc.

Countr

y

1 Foundations and Trends in

Machine Learning

j

9,855 14 4 10 559 152 10 11,00 139,75

2

IEEE Transactions on Pattern

Analysis and Machine

Intelligence

j

8,741 241 176 627 8.398 6.708 608 9,59 47,72

3 International Journal of

Computer Vision

j

6,298 141 135 291 5.146 1.688 277 5,62 38,12

4

ACM Transactions on

Intelligent Systems and

Technology

j

5,452 18 38 214 1.607 2.312 191 2,67 42,29

5 IEEE Transactions on Fuzzy

Systems

j

5,150 130 132 282 5.706 2.721 279 9,84 43,23

6 Journal of the ACM j

4,574 94 41 103 1.770 488 88 2,83 43,17

7 IEEE Transactions on Neural

Networks and Learning

j

3,323 137 351 576 8.375 3.456 568 5,59 23,86

Systems

8 Information Sciences j

3,286 103 867 1.401 35.421 7.371 1.377 4,97 40,85

9 Artificial Intelligence j

3,263 107 66 195 3.135 818 188 4,42 47,50

10 Cognitive Psychology j

2,948 85 32 66 2.426 324 64 4,87 75,81

11 Journal of Machine Learning

Research

j

2,929 114 91 696 4.026 2.232 681 2,37 44,24

12 Knowledge and Information

Systems

j

2,812 37 185 343 6.725 822 308 2,36 36,35

13 Pattern Recognition j

2,477 133 376 902 13.033 4.079 889 4,30 34,66

14 IEEE Intelligent Systems j

2,466 83 70 213 944 554 195 2,93 13,49

15 IEEE Transactions on Human-

Machine Systems

j

2,443 76 106 298 3.481 1.175 289 3,57 32,84

16 Journal of Memory and

Language

j

2,430 101 66 198 4.205 774 189 4,39 63,71

17 Computational Linguistics j

2,425 64 37 85 2.143 320 84 2,38 57,92

18 International Journal of

Approximate Reasoning

j

2,407 61 142 283 5.209 934 267 3,08 36,68

19 Machine Learning j

2,402 110 87 185 3.071 528 174 2,66 35,30

20 International Journal of

Robotics Research

j

2,256 97 94 299 3.724 1.405 278 4,49 39,62

21 Knowledge-Based Systems j

2,190 55 371 685 13.198 2.840 674 4,01 35,57

22 Swarm Intelligence j

2,056 18 14 41 607 154 37 3,92 43,36

23 Fuzzy Optimization and

Decision Making

j

2,006 32 31 74 536 201 70 3,08 17,29

24 Expert Systems with

Applications

j

1,996 98 721 3.867 30.946 13.669 3.803 3,33 42,92

25 Physics of Life Reviews j

1,974 34 125 274 3.260 293 35 7,57 26,08

26

IEEE Transactions on

Computational Intelligence

and AI in Games

j

1,965 22 33 80 1.135 301 78 3,51 34,39

27 Fuzzy Sets and Systems j

1,891 120 305 580 6.896 1.392 528 2,31 22,61

28 Journal of Artificial

Intelligence Research

j

1,725 81 64 174 3.131 436 174 2,29 48,92

29 Constraints j

1,567 32 27 53 783 103 52 1,46 29,00

30 Topics in Cognitive Science j

1,559 25 66 166 2.151 417 148 2,86 32,59

31 Journal of Scheduling j

1,549 42 67 164 1.231 254 151 1,38 18,37

32

Synthesis Lectures on

Artificial Intelligence and

Machine Learning

k

1,531 9 5 15 743 44 15 2,88 148,60

33 Journal of Automated

Reasoning

j

1,526 39 35 105 1.254 156 98 1,58 35,83

34 Autonomous Agents and

Multi-Agent Systems

j

1,525 49 59 101 2.694 248 95 2,60 45,66

35 Engineering Applications of

Artificial Intelligence

j

1,525 60 201 531 8.623 1.828 525 3,18 42,90

36 Theory and Practice of Logic

Programming

j

1,477 28 47 125 1.478 178 120 1,61 31,45

37 Applied Intelligence j

1,474 40 170 255 6.619 790 238 3,58 38,94

38

International Journal of

Machine Learning and

Cybernetics

j

1,446 19 64 144 2.180 413 143 1,50 34,06

39 Autonomous Robots j

1,411 73 68 148 2.772 532 140 3,74 40,76

40

Journal of the American

Society for Information

Science and Technology

j

1,399 93 33 624 1.502 1.410 546 2,26 45,52

41 Journal of Heuristics j

1,386 46 39 104 1.256 200 102 1,68 32,21

42 Cognitive Science j

1,365 73 73 192 4.209 518 189 2,30 57,66

43 Neural Networks j

1,303 100 151 457 5.549 1.438 435 3,58 36,75

44 Pattern Recognition Letters j

1,294 102 339 807 10.271 2.112 782 2,67 30,30

45 Journal of Intelligent

Manufacturing

j

1,277 50 258 456 7.743 825 391 2,03 30,01

46 Parallel Computing j

1,232 50 66 158 1.943 403 145 2,50 29,44

47 Advanced Engineering

Informatics

j

1,218 48 53 201 1.007 616 182 2,69 19,00

48 International Journal of

Intelligent Systems

j

1,215 53 60 181 2.010 401 174 2,10 33,50

49 Neurocomputing j

1,211 77 1.095 1.506 31.233 4.346 1.456 2,82 28,52

50 Design Studies j

1,184 58 25 92 1.274 240 90 2,03 50,96

Current Research and Breakthroughs: Here are the breakthroughs in artificial intelligence research from 2011-2015:

1. IBM Watson wins Jeopardy demo's integration of natural language processing, machine learning (ML), and big data. In 2011, IBM's AI system, dubbed "Watson," won a game of Jeopardy against the top two all-time champions. This was a historic moment, the "Kitty Hawk moment" for artificial intelligence. "It was really the first substantial, commercial demonstration of the power of this technology," explained Gold. "We wanted to prove a point that you could bring together some very unique technologies: natural language technologies, artificial intelligence, the context, the machine learning and deep learning, analytics and data and do something purposeful that ideally could be commercialized." 2. Siri/Google Now redefine human-data interaction. In the past few years, systems like Siri and Google Now opened our minds to the idea that we don't have to be tethered to a laptop to have seamless interaction with information. In this model, AIs will move from speech recognition to natural language interaction, to natural language generation, and eventually to an ability to write as well as receive information. 3. Deep learning demonstrates how machines learn on their own, advance and adapt. "Machine learning is about man assisting computers. Deep learning is about systems beginning to progress and learn on their own," says Gold. "Historically, systems have always been trained. They've been programmed. And, over time, the programming languages changed. We certainly moved beyond FORTRAN and BASIC, but we've always been limited to this idea of conventional rules and logic and structured data." As we move into the area of AI and cognitive computing, we're exploring the ability of computers to do more unaided/unassisted learning. 4. Image recognition and interpretation now rivals what humans can do — allowing for imagine interpretation and anomaly detection. Image recognition has exploded over the last few years. Facebook and Google Photos, for example, each have tens of billions of images on their platform. With this dataset, they (and many others) are developing technologies that go beyond facial recognition providing algorithms that can tell you what is in the image: a boat, plane, car, cat, dog, and so on. The crazy part is that the algorithms are better than humans at recognizing images. The implications are enormous. "Imagine," says Gold, "an AI able to examine an X-ray or CAT scan or MRI to report what looks abnormal." 5. AI Apps proliferate: universities scramble to adopt AI curriculum. As AI begins to impact every industry and every profession, there is a response where schools and universities are ramping up their AI and machine learning curriculum. IBM, for example, is working with over 150 partners to present both business and technology-oriented students with cognitive computing curricula. So what's in store for the near future? Anticipated Top AI Breakthroughs: 2016 – 2018 Here are Gold's predictions for the most exciting, disruptive developments coming in AI in the next three years. As entrepreneurs and investors, these are the areas you should be focusing on, as the business opportunities are tremendous. 1. Next-gen A.I. systems will beat the Turing Test Alan Turing created the Turing Test over half a century ago as a way to determine a machine's ability to exhibit intelligent behavior indistinguishable from that of a human. Loosely, if an artificial system passed the Turing Test, it could be considered "AI." Gold believes, "that for all practical purposes, these systems will pass the Turing Test" in the next three-year period. Perhaps more importantly, if it does, this event will accelerate the conversation about the proper use of these technologies and their applications. 2. All five human senses (yes, including taste, smell and touch) will become part of the normal computing experience. AIs will begin to sense and use all five senses. "The sense of touch, smell, and hearing will become prominent in the use of AI," explained Gold. "It will begin to process all that additional incremental information." When applied to our computing experience, we will engage in a much more intuitive and natural ecosystem that appeals to all of our senses. 3. Solving big problems: detect and deter terrorism, manage global climate change. AI will help solve some of society's most daunting challenges. Gold continues, "We've discussed AI's impact on healthcare. We're already seeing this technology being deployed in governments to assist in the understanding and preemptive discovery of terrorist activity." We'll see revolutions in how we manage climate change, redesign and democratize education, make scientific discoveries, leverage energy resources, and develop solutions to difficult problems. 4. Leverage ALL health data (genomic, phenotypic, social) to redefine the practice of medicine. "I think AI's effect on healthcare will be far more pervasive and far quicker than anyone anticipates," says Gold. "Even today, AI/machine learning is being used in oncology to identify optimal treatment patterns." But it goes far beyond this. AI is being used to match clinical trials with patients, drive robotic surgeons, read radiological findings and analyze genomic sequences. 5. AI will be woven into the very fabric of our lives — physically and virtually. Ultimately, during the AI revolution taking place in the next three years, AIs will be integrated into everything around us, combining sensors and networks and making all systems "smart." AIso will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalized and easy to use. We'll be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.

Top 5 Companies investing in AI in 2016: Microsoft, Apple, Google, Facebook, Amazon.

V. Ashwini Kumar Current Breakthroughs and Research January 02, 2017