Socially Sensitive Computing: A necessary Paradigm Shift for Computer Science Tom Addis Computer Science & Software Engineering University of Portsmouth.

Slides:



Advertisements
Similar presentations
Requirements gathering
Advertisements

Brief Introduction to Logic. Outline Historical View Propositional Logic : Syntax Propositional Logic : Semantics Satisfiability Natural Deduction : Proofs.
“Teach A Level Maths” Vol. 2: A2 Core Modules
1 CHAPTER 4 RELATIONAL ALGEBRA AND CALCULUS. 2 Introduction - We discuss here two mathematical formalisms which can be used as the basis for stating and.
Names and Bindings.
Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
Copyright © 2006 Addison-Wesley. All rights reserved. 3.5 Dynamic Semantics Meanings of expressions, statements, and program units Static semantics – type.
CS 355 – Programming Languages
OASIS Reference Model for Service Oriented Architecture 1.0
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
Comp 205: Comparative Programming Languages Semantics of Imperative Programming Languages denotational semantics operational semantics logical semantics.
CPSC 411, Fall 2008: Set 12 1 CPSC 411 Design and Analysis of Algorithms Set 12: Undecidability Prof. Jennifer Welch Fall 2008.
1 Polynomial Church-Turing thesis A decision problem can be solved in polynomial time by using a reasonable sequential model of computation if and only.
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
Copyright © 2006 Addison-Wesley. All rights reserved.1-1 ICS 410: Programming Languages Chapter 3 : Describing Syntax and Semantics Operational Semantics.
Ghent, July 6, Evaluating a COSMIC-FFP Measurement Procedure for Multi-Layer Object-Oriented Conceptual Schemas Simon Claeys (Master student Ghent.
1 CSE 417: Algorithms and Computational Complexity Winter 2001 Lecture 18 Instructor: Paul Beame.
Describing Syntax and Semantics
Predicates and Quantifiers
The Linguistic Turn To what extent is knowledge in the use of language rather than what language is about? MRes Philosophy of Knowledge: Day 2 - Session.
Chapter 6: Objections to the Physical Symbol System Hypothesis.
Artificial Intelligence: Definition “... the branch of computer science that is concerned with the automation of intelligent behavior.” (Luger, 2009) “The.
CSC3315 (Spring 2009)1 CSC 3315 Programming Languages Hamid Harroud School of Science and Engineering, Akhawayn University
11 C H A P T E R Artificial Intelligence and Expert Systems.
MATH 224 – Discrete Mathematics
CS 173, Lecture B August 27, 2015 Tandy Warnow. Proofs You want to prove that some statement A is true. You can try to prove it directly, or you can prove.
Korea Advanced Institute of Science and Technology, Dept. of EECS, Div. of CS, Information Systems Lab. 1/10 CS204 Course Overview Prof.
MA/CSSE 474 Theory of Computation More Reduction Examples Non-SD Reductions.
1 Sequential Machine Theory Prof. K. J. Hintz Department of Electrical and Computer Engineering Lecture 1 Adaptation to this.
Pattern-directed inference systems
1 Introduction to Software Engineering Lecture 1.
COGNITIVE SEMANTICS: INTRODUCTION DANA RETOVÁ CSCTR2010 – Session 1.
A Context Model based on Ontological Languages: a Proposal for Information Visualization School of Informatics Castilla-La Mancha University Ramón Hervás.
Artificial Intelligence: Introduction Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
Formal Specification and Analysis of Software Architectures Using the Chemical Abstract Machine Model CS 5381 Juan C. González Authors: Paola Inverardi.
3.2 Semantics. 2 Semantics Attribute Grammars The Meanings of Programs: Semantics Sebesta Chapter 3.
Programming Languages and Design Lecture 3 Semantic Specifications of Programming Languages Instructor: Li Ma Department of Computer Science Texas Southern.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Chapter 1 –Defining AI Next Tuesday –Intelligent Agents –AIMA, Chapter 2 –HW: Problem.
Chapter 2 With Question/Answer Animations. Section 2.1.
Predicates and Quantifiers Dr. Yasir Ali. 1.Predicates 2.Quantifiers a.Universal Quantifiers b.Existential Quantifiers 3.Negation of Quantifiers 4.Universal.
Cs7100 (Prasad)L2SpecIntro1 Motivation for Language Specification.
Programming Language Descriptions. What drives PL Development? Computers are “in charge” of extremely important issues Execute a program literally. Exercise.
Predicate Logic One step stronger than propositional logic Copyright © Curt Hill.
Feng Zhiyong Tianjin University Fall  An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that.
LDK R Logics for Data and Knowledge Representation Propositional Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Albert Gatt LIN3021 Formal Semantics Lecture 3. Aims This lecture is divided into two parts: 1. We make our first attempts at formalising the notion of.
ARTIFICIAL INTELLIGENCE Lecture 2 Propositional Calculus.
CMPB454 ARTIFICIAL INTELLIGENCE (AI) CHAPTER 1.1 Background Information CHAPTER 1.1 Background Information Instructor: Alicia Tang Y. C.
FUNCTIONS COSC-1321 Discrete Structures 1. Function. Definition Let X and Y be sets. A function f from X to Y is a relation from X to Y with the property.
Formal Semantics Purpose: formalize correct reasoning.
CSC3315 (Spring 2009)1 CSC 3315 Languages & Compilers Hamid Harroud School of Science and Engineering, Akhawayn University
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
August 2003 CIS102/LECTURE 9/FKS 1 Mathematics for Computing Lecture 9 LOGIC Chapter 3.
CS 173, Lecture B August 27, 2015 Tandy Warnow. Proofs You want to prove that some statement A is true. You can try to prove it directly, or you can prove.
Formal Methods. What Are Formal Methods Formal methods refers to a variety of mathematical modeling techniques that are applicable to computer system.
The Church-Turing Thesis Chapter Are We Done? FSM  PDA  Turing machine Is this the end of the line? There are still problems we cannot solve:
MDD-Kurs / MDA Cortex Brainware Consulting & Training GmbH Copyright © 2007 Cortex Brainware GmbH Bild 1Ver.: 1.0 How does intelligent functionality implemented.
A Probabilistic Quantifier Fuzzification Mechanism: The Model and Its Evaluation for Information Retrieval Felix Díaz-Hemida, David E. Losada, Alberto.
Advanced Computer Systems
CS 326 Programming Languages, Concepts and Implementation
Representation, Syntax, Paradigms, Types
Counting Sets.
Semantics In propositional logic, we associate atoms with propositions about the world. We specify the semantics of our logic, giving it a “meaning”. Such.
Representation, Syntax, Paradigms, Types
Logics for Data and Knowledge Representation
Representation, Syntax, Paradigms, Types
Discrete Mathematics Lecture 4 & 5: Predicate and Quantifier
Motivation for Language Specification
COMPILER CONSTRUCTION
Presentation transcript:

Socially Sensitive Computing: A necessary Paradigm Shift for Computer Science Tom Addis Computer Science & Software Engineering University of Portsmouth Bart-Floris Visscher Computer Science & Software Engineering University of Portsmouth Dave Billinge Creative Technology University of Portsmouth “Rules are for the obedience of fools and the guidance of wise men” Douglas Bader (1910 –1982)

30th March 2004GC7 Non-Classical Computing2 Formal Semantics Church-Turing Thesis: Church-Turing Thesis: Equivalent Representation Systems Equivalent Representation Systems Computer program (Turing Machines) Computer program (Turing Machines) Functional Statements (Functional Machines) Functional Statements (Functional Machines) Tractatus Propositions (Predicate Machines) Tractatus Propositions (Predicate Machines) It follows that: It follows that: Everything is potentially unambiguously describable Everything is potentially unambiguously describable All sets are rational (countable) All sets are rational (countable) Set membership is always specifiable and context independent or has an explicit context Set membership is always specifiable and context independent or has an explicit context Fuzzy (ordinal) and probabilistic sets (ratio of integers) are countable Fuzzy (ordinal) and probabilistic sets (ratio of integers) are countable

30th March 2004GC7 Non-Classical Computing3 Consequences of Formal Model Practical Practical Any set of names can be used in a program to represent a proposition. Any set of names can be used in a program to represent a proposition. There is an infinite but bounded set of possible organisations of a program. There is an infinite but bounded set of possible organisations of a program. There is a such a thing as a ‘minimum program’ There is a such a thing as a ‘minimum program’ Programs can only have one interpretation. Programs can only have one interpretation. Social Social Rules can be constructed that can describe unambiguously any situation. Thus: Rules can be constructed that can describe unambiguously any situation. Thus: Rules can bypass human judgement. Rules can bypass human judgement. There is only one correct way to see the world There is only one correct way to see the world

30th March 2004GC7 Non-Classical Computing4 Problem 1 Programs can only have a single interpretation. Programs can only have a single interpretation. But programs have at least two interpretations But programs have at least two interpretations The Computer State The Computer State The Problem Domain The Problem Domain Program Problem Domain Computer States (bits)

30th March 2004GC7 Non-Classical Computing5 Formal Interpretation Mapping onto Wittgenstein’s Objects. Mapping onto Wittgenstein’s Objects. Independent Independent Atomic Atomic Exist in all possible worlds Exist in all possible worlds Immaterial Immaterial Indescribable Indescribable Self governed Self governed The bit has all these properties The bit has all these properties Such objects are rarely in the Problem Domain Such objects are rarely in the Problem Domain Program Computer States (bits) The only rational interpretation of a program

30th March 2004GC7 Non-Classical Computing6 Problem 2 Everything is NOT potentially unambiguously describable Everything is NOT potentially unambiguously describable There are also irrational sets (not countable). There are also irrational sets (not countable). Some sets depend upon human usage and context. Some sets depend upon human usage and context. Examples: Examples: Games, chairs and life Games, chairs and life A Chair Jean-Francois Dupris Chair Specification: Designed specifically to be sat upon, Stands on its own Has four legs Has a back Sitter’s Feet touches floor Chair Specification: Designed specifically to be sat upon Stands on its own Has four legs Has a back Chair Specification: Designed specifically to be sat upon Stands on its own Chair Specification: Designed specifically to be sat upon Chair Specification: Designed to be sat upon Chair Specification:

30th March 2004GC7 Non-Classical Computing7 Current Status of Computer Science We have computer programs with a semantics based upon computer bits. We have computer programs with a semantics based upon computer bits.and We create programs that cannot rationally be assigned meaning to the very problem domain for which we write them. We create programs that cannot rationally be assigned meaning to the very problem domain for which we write them.

30th March 2004GC7 Non-Classical Computing8 Computing with Irrational Sets 1 Programs must remain in the domain of rational sets. Programs must remain in the domain of rational sets. We have the freedom to use the program’s accidental properties: We have the freedom to use the program’s accidental properties: The choice of names The choice of names The choice of program organisation The choice of program organisation The choices are used to provide a semantic link with the problem domain. The choices are used to provide a semantic link with the problem domain.

30th March 2004GC7 Non-Classical Computing9 Practical Consequences Choice of program names and organisations must be flexible. Choice of program names and organisations must be flexible. The dynamics of program names, organisations and assignment of meaning must be linked to a social system. The dynamics of program names, organisations and assignment of meaning must be linked to a social system. A method for a ‘minimum program’ is helpful. A method for a ‘minimum program’ is helpful. The problems underlying such issues as natural language understanding will be significantly reduced. The problems underlying such issues as natural language understanding will be significantly reduced. There will be the possibility of having a truly creative machine. There will be the possibility of having a truly creative machine.

30th March 2004GC7 Non-Classical Computing10 Computing with Irrational Sets 2 Minimum Program Computer States (bits) Problem Domain Names & Organisation Social sensitive feedback Contexts allows the use of rational sets The problem domain contains irrational sets. So we cannot use denotational semantics The problem domain contains irrational sets. So we cannot use denotational semantics

30th March 2004GC7 Non-Classical Computing11 Social Consequences Since rules cannot be devised that are unambiguous Since rules cannot be devised that are unambiguous then there will always be a need for human judgement. then there will always be a need for human judgement. There will always be a wide range of perceptions of a problem domain There will always be a wide range of perceptions of a problem domain that cannot be predicted that cannot be predicted and hence planned for. and hence planned for.

30th March 2004GC7 Non-Classical Computing12 Two Semantics of Irrational Sets Wittgenstein: Wittgenstein: Use family resemblance instead of sets. Use family resemblance instead of sets. Use word usage (and structures) instead of reference. Use word usage (and structures) instead of reference. Lakoff (and Johnson): Lakoff (and Johnson) 1 : Use prototypes (paradigms) Use prototypes (paradigms) Use metaphor instead of reference Use metaphor instead of reference [1] Lakoff and Johnson (1980) – Metaphors we live by Lakoff (1986) – Women, Fire and Dangerous Things The Challenge is how do you do this?