Introduction to AI & AI Principles (Semester 1) WEEK 6 (07/08) John Barnden Professor of Artificial Intelligence School of Computer Science University.

Slides:



Advertisements
Similar presentations
Lecture Notes on AI & NN Chapter 1 Introduction to Intelligence Theory Section 2 Intelligence Theory & Information Science.
Advertisements

1 Knowledge Representation Introduction KR and Logic.
Heuristic Search techniques
Ch 4. Heuristic Search 4.0 Introduction(Heuristic)
Semantics (Representing Meaning)
Situation Calculus for Action Descriptions We talked about STRIPS representations for actions. Another common representation is called the Situation Calculus.
Week 11 Review: Statistical Model A statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution.
CPSC 322 Introduction to Artificial Intelligence November 5, 2004.
ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.
Best-First Search: Agendas
CPSC 322 Introduction to Artificial Intelligence October 29, 2004.
Introduction to AI & AI Principles (Semester 1) WEEK 3 (07/08) John Barnden Professor of Artificial Intelligence School of Computer Science University.
LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.
Introduction to AI & AI Principles (Semester 1) WEEK 6 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Introduction to AI & AI Principles (Semester 1) WEEK 8 (07/08) [Barnden’s slides only] John Barnden Professor of Artificial Intelligence School of Computer.
Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Introduction to AI & AI Principles (Semester 1) REVISION LECTURES (Term 3) John Barnden Professor of Artificial Intelligence School of Computer Science.
Introduction to AI & AI Principles (Semester 1) WEEK 5 (07/08) John Barnden Professor of Artificial Intelligence School of Computer Science University.
Introduction to AI & AI Principles (Semester 1) WEEK 7 (07/08) [Barnden’s slides only] John Barnden Professor of Artificial Intelligence School of Computer.
Introduction to AI & AI Principles (Semester 1) WEEK 2 – Tuesday part B Introduction to AI & AI Principles (Semester 1) WEEK 2 – Tuesday part B (2008/09)
Introduction to AI & AI Principles (Semester 1) REVISION LECTURES (Term 3) John Barnden Professor of Artificial Intelligence School of Computer Science.
Introduction to AI & AI Principles (Semester 1) WEEK 10 (07/08) [John Barnden’s slides only] School of Computer Science University of Birmingham, UK.
Introduction to AI & AI Principles (Semester 1) WEEK 7 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Introduction to AI & AI Principles (Semester 1) WEEK 11 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Introduction to AI & AI Principles (Semester 1) WEEK 3 – Wednesday Introduction to AI & AI Principles (Semester 1) WEEK 3 – Wednesday (2008/09) John Barnden.
Introduction to AI & AI Principles (Semester 1) WEEK 4 (07/08) John Barnden Professor of Artificial Intelligence School of Computer Science University.
Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Introduction to AI & AI Principles (Semester 1) WEEK 1 – Wednesday Introduction to AI & AI Principles (Semester 1) WEEK 1 – Wednesday (2008/09) John Barnden.
Introduction to AI & AI Principles (Semester 1) WEEK 3 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Introduction to AI & AI Principles (Semester 1) WEEK 3 – Tuesday part B Introduction to AI & AI Principles (Semester 1) WEEK 3 – Tuesday part B (2008/09)
Introduction to AI & AI Principles (Semester 1) WEEK 4 – Wednesday Introduction to AI & AI Principles (Semester 1) WEEK 4 – Wednesday (2008/09) John Barnden.
Introduction to AI & AI Principles (Semester 1) WEEK 5 – Wednesday Introduction to AI & AI Principles (Semester 1) WEEK 5 – Wednesday (2008/09) John Barnden.
Introduction to AI & AI Principles (Semester 1) WEEK 3 – Tuesday part A Introduction to AI & AI Principles (Semester 1) WEEK 3 – Tuesday part A (2008/09)
Philosophy A philosophy is a system of beliefs about reality.
Intro to Maths for CS: 2013/14 Sets (2) – OPTIONAL MATERIAL John Barnden Professor of Artificial Intelligence School of Computer Science University of.
Course Overview  What is AI?  What are the Major Challenges?  What are the Main Techniques?  Where are we failing, and why?  Step back and look at.
Artificial Intelligence in Game Design Problems and Goals.
Artificial Intelligence Introduction (2). What is Artificial Intelligence ?  making computers that think?  the automation of activities we associate.
CONTEMPLATION, INQUIRY, AND CREATION: HOW TO TEACH MATH WHILE KEEPING ONE’S MOUTH SHUT Andrew-David Bjork Siena Heights University 13 th Biennial Colloquium.
1 State Space of a Problem Lecture 03 ITS033 – Programming & Algorithms Asst. Prof.
Artificial Intelligence
Fundamentals/ICY: Databases 2013/14 Week 10 –Monday –Normalization, contd John Barnden Professor of Artificial Intelligence School of Computer Science.
Knowledge Representation CPTR 314. The need of a Good Representation  The representation that is used to represent a problem is very important  The.
1 CS 350 Data Structures Chaminade University of Honolulu.
LOGIC AND ONTOLOGY Both logic and ontology are important areas of philosophy covering large, diverse, and active research projects. These two areas overlap.
Fundamentals/ICY: Databases 2012/13 WEEK 11 – 4 th Normal Form (optional material) John Barnden Professor of Artificial Intelligence School of Computer.
Introduction to AI & AI Principles (Semester 1) REVISION WEEK 1 (2008/09) John Barnden Professor of Artificial Intelligence School of Computer Science.
Yarmouk University Department of Computer Information Systems CIS 499 Yarmouk University Department of Computer Information Systems CIS 499 Yarmouk University.
1 Introduction to Artificial Intelligence (Lecture 1)
Lecture 11 Data Structures, Algorithms & Complexity Introduction Dr Kevin Casey BSc, MSc, PhD GRIFFITH COLLEGE DUBLIN.
Fundamentals/ICY: Databases 2013/14 WEEK 9 –Friday John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Instructional Objective  Define an agent  Define an Intelligent agent  Define a Rational agent  Discuss different types of environment  Explain classes.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Chapter 1 –Defining AI Next Tuesday –Intelligent Agents –AIMA, Chapter 2 –HW: Problem.
University of Kurdistan Artificial Intelligence Methods (AIM) Lecturer: Kaveh Mollazade, Ph.D. Department of Biosystems Engineering, Faculty of Agriculture,
Intro to Planning Or, how to represent the planning problem in logic.
1 Core English 1 Listening Task – p 158 Rhetorical Function Questions.
Intro to Maths for CS: 2012/13 Sets (end, week 3) John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,
Forward and Backward Chaining
Intelligent Agents Chapter 2. How do you design an intelligent agent? Definition: An intelligent agent perceives its environment via sensors and acts.
1 Software Requirements Descriptions and specifications of a system.
ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.
Fundamentals/ICY: Databases 2010/11 WEEK 1
Artificial Intelligence Lecture No. 5
Knowledge Representation
Intelligent Agents Chapter 2.
STATE SPACE REPRESENTATION
KNOWLEDGE REPRESENTATION
Workshop for Programming And Systems Management Teachers
Search.
Search.
Presentation transcript:

Introduction to AI & AI Principles (Semester 1) WEEK 6 (07/08) John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK

Making a Hot Drink uYour suggestions please!

Moving Around and Performing Actions (review) uRemembering a “mental map” of some sort and knowing where oneself is in such a map. Keeping track of movements. Recognizing landmarks. uCreating such a map. uMoving arms, etc. to reach objects efficiently and safely. uGrasping (etc.) objects safely.

Planning Actions: Examples uPlanning is discussed in Callan ch. 9 (and 10). uExamples of planning: l Planning the sequence of steps needed to buy presents for people. l Planning how to get to a particular place. l Planning the steps needed to build something. l Planning moves in a game (whether chess, a shoot-em-up, football, …) l Planning the steps needed to convince somebody of something.

Planning Actions: Some Needs uEnvisaging the effect of a series of actions. uRemembering different series of actions and their envisaged effects, so as to investigate alternatives properly. uTaking account of time constraints, effort constraints, etc. uTaking account of interactions between parts of the problem (preconditions, conflicts). uRecovering from unexpected problems and benefits when executing a plan: (partial) re-planning, incl. because of unexpected changes in the world independent of one’s own actions. uAllowing for “known unknowns” (e.g., action effects that you know you don’t know).

Planning: Towards “Search” uSearch is covered in Callan ch. 3. 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 … … … …

Towards Search, contd. uWhat order to investigate the actions that are possible from 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?

More on Search in a Later Lecture

Representation Needs in Planning uRepresenting the actual state of the “world”. uKeeping track of several hypothetical states and how they arise from each other. uRepresenting all the information needed about each possible action the system can take. This includes information about what preconditions need to hold in order for the action to apply, and what the effects of the action are (effects on world and on system itself, incl. the “cost” to the system). uRepresenting the goal(s) conditions or states to be achieved, sub-goal states that dynamically arise, time constraints, effort constraints, etc. uPossibly, representing relationships between actions such as conflicts. uInternally expressing general knowledge about the world (e.g., if it’s raining and I go outside my joints will rust).

Representation Needs, contd. uPossibly, remembering useful things to help further planning (a type of learning): l Useful, recurring sequences of actions (“chunking” of actions) l Abstractions from such sequences l Why (parts of) the plan succeeded l What failed and why l Why particular steps were decided upon.

Further Representation Needs (for Planning or Other Purposes) uInferential Adequacy (has also been called Heuristic Adequacy): ability adequately to support processes for deriving new information from existing information (“inference”, “reasoning”). uAbility to include special things that, for example, speed up access, inference, learning, … uAppropriate degree of narrowness or breadth (general- purposeness) for the researcher’s aims.

Why Not Use Human Language? (further thoughts) uThe need for a lot of context to remove ambiguity. Difficulty of knowing exactly what the context is. uPossibly leads to incorrectness or internal misunderstanding. uAlso adds complexity and uncertainty that hurts inferential adequacy. uThe syntax (grammatical structure) of human language is complex and full of historical quirks. This is a problem for all processing of the language, including inference.

Representing a State of the World and Expressing General Knowledge about the World (for planning or other purposes) uA state could be past, present, future, hypothetical, … Ignore those differences for the moment.

Need to … u … represent entities (physical things, mental things, abstract things, situations, events, actions, processes, …), properties of entities, relationships between entities, groups of entities, … u … make generalizations about types of entities u … capture propositional structure of information.

Entities: Some Examples uPeople, desks, faces, noses, pens, chess-pieces, windows, light-switches, rooms, buildings, towns, land areas, planets, … uSizes, lengths, weights, times, prices, …, numbers uWritten/spoken words/numbers/…, diagrams, … uThoughts, emotions, claims, prejudices, personality types, plans, strategies, political movements, terrorism, peace, justice, … uActs of eating, eating in general, the concept of eating, … uSimilarly of saying, believing, learning, …

Properties: Some Examples uBeing tall, being expensive, being stupid, having two legs, being kind, being a prime number, being a dog, being an act of violence, having a tail, being coffee, …

Relationships: Some Examples uX loving Y, X kissing Y, Y slapping X, X being married to Z, X being taller than Y, X drinking Y, X being a friend of Y, X being a square root of Y, X being less time-consuming than Y, X’s number of legs being Y, X being the end-point of Y, X’s hand grasping Y, uX being between Y and Z, X being the path from Y to Z, X’s tentacle number Z grasping Y, X giving money- amount Y to charity Z uX kissing Y at time T uX being stupid at time T, X giving money-amount Y to charity Z at time T

Entities versus Properties versus Relationships uPartly a matter of taste and convenience whether you think of something as being a property of one or more things or a relationship between things. l X being stupid at time T: timed property of X, or a relationship between X and T. l X having 2 legs: a property of X, or a relationship between X and 2. l X and Y being friends as a relationship between X and Y, or a property of X and/or a property of Y, or a property of the group consisting of X and Y uProperties and relationships are also, in principle, entities. But usually the entities are confined to those that we want to state properties of or relationships between.