DOCSLIB.ORG

ABSTRACT Title of Dissertation: FRAME PROBLEMS

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ABSTRACT Title of Dissertation: FRAME PROBLEMS ABSTRACT Title of dissertation: FRAME PROBLEMS, FODOR‟S CHALLENGE, AND PRACTICAL REASON Erich C. Deise, Doctor of Philosophy, 2008 Dissertation directed by: Professor Peter Carruthers Department of Philosophy By bringing the frame problem to bear on psychology, Fodor argues that the interesting activities of mind are not amenable to computational modeling. Following exegesis of the frame problem and Fodor‟s claims, I argue that underlying Fodor‟s argument is an unsatisfiable normative principle of rationality that in turn commits him to a particular descriptive claim about the nature of our minds. I argue that the descriptive claim is false and that we should reject the normative principle in favor of one that is at least in principle satisfiable. From this it follows, I argue, that we have no reason for thinking the activities of our minds to be, as a matter of principle, unmodelable. Drawing upon Baars‟ Global Workspace theory, I next outline an alternative framework that provides a means by which the set of engineering challenges raised by Fodor might be met. Having sketched this alternative, I turn next to consider some of the frame problems arising in practical reason and decision-making. Following discussion of the nature of emotion and its influence on practical reason and decision- making, I argue that consideration of emotion provides one means by which we might contend with some of the frame problem instances that arise in that domain. FRAME PROBLEMS, FODOR‟S CHALLENGE, AND PRACTICAL REASON By Erich C. Deise Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirement for the degree of Doctor of Philosophy 2008 Advisory Committee: Professor Peter Carruthers, Chair Professor Christopher Cherniak Professor Michael Dougherty Professor Jeff Horty Professor Paul Pietroski © Copyright Erich C. Deise 2008 DEDICATION For my family, old and new. To my parents Jerry and Sue, wife Alexis, and son Rainer. ii ACKNOWLEDGMENTS I am particularly indebted to Peter Carruthers, whose insights, mentoring and advice have been invariably helpful and whose patience, support and encouragement have been nothing less than supererogatory. I have profited immensely from our many discussions and from the detailed comments provided by him throughout the process. Over the years, Chris Cherniak has been a source of much insight and guidance, as well. I have both enjoyed and benefitted greatly from our many discussions and thank him for, so long ago, setting me down the path that led to this essay. Both are owed special thanks for all that they have done. I am also grateful to Jeff Horty both for helping me to better understand the formal intricacies of the planning problem and for his commentary on Chapter 1. Likewise, I would like to thank Paul Pietroski for helping me to better understand a particular facet of Fodor‟s argument and for his willingness to serve, on short notice, as a member of the examining committee. Thanks as well to William Gasarch of the University of Maryland Department of Computer Science both for helping me to understand some of the finer points of computational complexity theory and for assisting me in locating a version of the problem within the complexity hierarchy. I am also grateful to Georges Rey for providing extensive commentary on a number of chapters. While he endorses few, if any, of my conclusions, his suggestions and objections were invaluable in helping me to better structure the project. I also wish to thank Patricia Greenspan both for her guidance early on and for her comments on several early draft chapters. I have benefitted from a number of discussions with other philosophers, computer scientists and cognitive neuroscientists. Among these, I would like to thank Michael Morreau, Scott James and Sotiros Kotsanis, of the University of Maryland philosophy department, David Poeppel of the NACS program, Ernie Davis of the Department of Computer Science at New York University as well as audiences at the University of Maryland. Philip Ryan deserves thanks for taking on the onerous and arduous task of proofreading the submission draft. Finally, I am, in every way, indebted to my parents Jerry and Sue and my wife Alexis for everything else. iii TABLE OF CONTENTS Dedication …………………………………………………………………………………… ii Acknowledgements ………………………………………………………………………... iii Introduction ………………………………………………………………………………… 1 Chapter 1: Introducing the frame problem ……………………………………………... 9 Chapter 2: Fodor‟s challenge, frame problems & computational psychology ……… 40 Chapter 3: Rational systems and Fodor‟s pessimism ………………………………….. 64 Chapter 4: On (the very idea) of bringing emotional affect to bear ………………….. 115 Chapter 5: Emotion and some frame problem instances ……………………………… 161 Conclusion ………………………………………………………………………………….. 242 Bibliography ………………………………………………………………………………... 249 iv INTRODUCTION Drawing upon the frame problem of A.I., Fodor argues, quite famously, that the “interesting” activities of mind are not amenable to computational modeling. I will argue that we have no reason for thinking Fodor to have made the case for his pessimistic conclusion with respect to the unmodelability of our minds. Furthermore, I suggest that there are plausible alternatives available that once explicated and brought to bear might provide a means by which at least some of the “interesting” operations of mind might come to be modeled in computationally feasible terms. In Chapter 1, I provide an overview of the frame problem. Both the original formal incarnation of problem that arose in artificial intelligence and various philosophical interpretations or variants of the problem are reviewed. I next set out the underlying structure of the puzzle, arguing that “the” frame problem is not one problem but is instead a constellation of related problem instances. In Chapter 2, I consider in greater detail Fodor‟s version of the frame problem as well as his application of it to computational psychology. In presenting Fodor‟s argument for his pessimistic conclusion - that the interesting activities of mind are not amenable to computational modeling – I disentangle a number of distinct argument strains. In so doing, I argue that Fodor‟s argument relies, at base, upon a particular normative principle of rationality. This normative claim, I argue in turn, commits Fodor to a particular descriptive claim about the nature of our cognitive processes. In Chapter 3, I focus on assessing Fodor‟s argument. Briefly, Fodor argues that our cognitive processes are unmodelable because we at least sometimes arrive at conclusions rationally. Since anything that arrives at conclusions rationally, he continues, must be capable of solving the frame problem and no system that solves that 1 problem can be modeled, it follows, he concludes, that our cognitive processes are not amenable to modeling. I argue that the normative principle of rationality undergirding Fodor‟s argument is untenable because it is in principle unsatisfiable. I next argue that the descriptive claim – that we ever arrive at conclusions rationally (in Fodor‟s sense) – is false. Taken together, I conclude that it is more reasonable to think that we should reject Fodor‟s normative rationality principle as overly demanding in favor of one that is, at least in principle, satisfiable by the likes of us. Any weakening of the demands of this rationality principle, I argue, effectively undermines Fodor‟s argument for the pessimistic conclusion. Specifically, by weakening the demands of the normative rationality principle, I argue that Fodor‟s arguments against the feasibility of the massive modularity of mind and heuristics approaches to modeling are no longer compelling. And so, I conclude that since we have no reasons for rejecting these approaches tout court, as Fodor suggests due to their “irrationality,” we have no reason of principle for thinking the operations of mind not amenable to modeling. There are, however, a number of in practice (as opposed to in principle) challenges raised by Fodor with which any proposed strategy for modeling the operations of mind must contend. To this end, I will consider an alternative model – namely Baars‟ Global Workspace framework. I will start by setting out Baars‟ account and turn next to considering how bringing this approach to bear might help in contending with some of the particular engineering challenges (e.g., the input routing and heuristic selection problems) raised by Fodor. By way of further fleshing out Baars‟ proposal, I will consider both Gigerenzer‟s “fast and frugal” heuristic approach and Barrett‟s discussion of modules as metaphorical enzymatic systems. When these 2 accounts are integrated into Global Workspace theory, a picture begins to emerge as to how a computationally modelable system might contend with the input routing and heuristic selections problems without the need for a central (and thus frame-problem- infected) executive to be posited. And so, when fleshed out, I suggest that Global Workspace theory might provide a plausible account, and an alternative unconsidered by Fodor, of how at least some of the “interesting” activities of mind might come to be modeled. In Chapters 4 and 5, I consider in greater detail some instances of the frame problem that arise in practical reason and decision-making, ultimately suggesting that emotion might be one means by which we contend with some of the problems arising in this domain. I begin this discussion, however, by
Load more

Recommended publications
  • The Dramatic True Story of the Frame Default
    The Dramatic True Story of the Frame Default Vladimir Lifschitz University of Texas at Austin Abstract This is an expository article about the solution to the frame problem pro- posed in 1980 by Raymond Reiter. For years, his “frame default” remained untested and suspect. But developments in some seemingly unrelated areas of computer science—logic programming and satisfiability solvers—eventually exonerated the frame default and turned it into a basis for important appli- cations. 1 Introduction This is an expository article about the dramatic story of the solution to the frame problem proposed in 1980 by Raymond Reiter [22]. For years, his “frame default” remained untested and suspect. But developments in some seemingly unrelated ar- eas of computer science—logic programming and satisfiability solvers—eventually exonerated the frame default and turned it into a basis for important applications. This paper grew out of the Great Moments in KR talk given at the 13th Inter- national Conference on Principles of Knowledge Representation and Reasoning. It does not attempt to provide a comprehensive history of research on the frame problem: the story of the frame default is only a small part of the big picture, and many deep and valuable ideas are not even mentioned here. The reader can learn more about work on the frame problem from the monographs [17, 21, 24, 25, 27]. 2 What Is the Frame Problem? The frame problem [16] is the problem of describing a dynamic domain without explicitly specifying which conditions are not affected by executing actions. Here is a simple example. Initially, Alice is in the room, and Bob is not.
    [Show full text]
  • Why Dreyfus' Frame Problem Argument Cannot Justify Anti
    Why Dreyfus’ Frame Problem Argument Cannot Justify Anti- Representational AI Nancy Salay ([email protected]) Department of Philosophy, Watson Hall 309 Queen‘s University, Kingston, ON K7L 3N6 Abstract disembodied cognitive models will not work, and this Hubert Dreyfus has argued recently that the frame problem, conclusion needs to be heard. By disentangling the ideas of discussion of which has fallen out of favour in the AI embodiment and representation, at least with respect to community, is still a deal breaker for the majority of AI Dreyfus‘ frame problem argument, the real locus of the projects, despite the fact that the logical version of it has been general polemic between traditional computational- solved. (Shanahan 1997, Thielscher 1998). Dreyfus thinks representational cognitive science and the more recent that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, embodied approaches is revealed. From this, I hope that instead, a thoroughly Heideggerian model of cognition, in productive debate will ensue. particular one that does not appeal to representations. I argue The paper proceeds in the following way: in section I, I that Dreyfus is too hasty in his condemnation of all describe and distinguish the logical version of the frame representational views; the argument he provides licenses problem and the philosophical one that remains unsolved; in only a rejection of disembodied models of cognition. In casting his net too broadly, Dreyfus circumscribes the section II, I rehearse Dreyfus‘ negative argument, what I‘ll cognitive playing field so closely that one is left wondering be calling his frame problem argument; in section III, I how his Heideggerian alternative could ever provide a highlight some key points from Dreyfus‘ positive account of foundation explanatorily robust enough for a theory of a Heideggerian alternative; in section IV, I make my case cognition.
    [Show full text]
  • Answers to Exercises
    Appendix A Answers to Exercises Answers to some of the exercises can be verified by running CLINGO, and they are not included here. 2.1. (c) Replace rule (1.1) by large(C) :- size(C,S), S > 500. 2.2. (a) X is a child of Y if Y is a parent of X. 2.3. (a) large(germany) :- size(germany,83), size(uk,64), 83 > 64. (b) child(dan,bob) :- parent(bob,dan). 2.4. (b), (c), and (d). 2.5. parent(ann,bob; bob,carol; bob,dan). 2.8. p(0,0*0+0+41) :- 0 = 0..3. 2.10. p(2**N,2**(N+1)) :- N = 0..3. 2.11. (a) p(N,(-1)**N) :- N = 0..4. (b) p(M,N) :- M = 1..4, N = 1..4, M >= N. 2.12. (a) grandparent(X,Z) :- parent(X,Y), parent(Y,Z). 2.13. (a) sibling(X,Y) :- parent(Z,X), parent(Z,Y), X != Y. 2.14. enrolled(S) :- enrolled(S,C). 2.15. same_city(X,Y) :- lives_in(X,C), lives_in(Y,C), X!=Y. 2.16. older(X,Y) :- age(X,M), age(Y,N), M > N. 2.18. Line 5: noncoprime(N) :- N = 1..n, I = 2..N, N\I = 0, k\I = 0. Line 10: coprime(N) :- N = 1..n, not noncoprime(N). © Springer Nature Switzerland AG 2019 149 V. Lifschitz, Answer Set Programming, https://doi.org/10.1007/978-3-030-24658-7 150 A Answers to Exercises 2.19. Line 6: three(N) :- N = 1..n, I = 0..n, J = 0..n, K = 0..n, N=I**2+J**2+K**2.
    [Show full text]
  • Temporal Projection and Explanation
    Temporal Projection and Explanation Andrew B. Baker and Matthew L. Ginsberg Computer Science Department Stanford University Stanford, California 94305 Abstract given is in fact somewhat simpler than that presented in [Hanks and McDermott, 1987], but still retains all of the We propose a solution to problems involving troublesome features of the original. In Section 3, we go temporal projection and explanation (e.g., the on to describe proposed solutions, and investigate their Yale shooting problem) based on the idea that technical shortcomings. whether a situation is abnormal should not de• The formal underpinnings of our own ideas are pre• pend upon historical information about how sented in Section 4, and we return to the Yale shooting the situation arose. We apply these ideas both scenario in Section 5, showing that our notions can be to the Yale shooting scenario and to a blocks used to solve both the original problem and the variant world domain that needs to address the quali• presented in Section 2. In Section 6, we extend our ideas fication problem. to deal with the qualification problem in a simple blocks world scenario. Concluding remarks are contained in 1 Introduction Section 7. The paper [1987] by Hanks and McDermott describing 2 The Yale shooting the Yale shooting problem has generated such a flurry of responses that it is difficult to imagine what another The Yale shooting problem involves reasoning about a one can contribute. The points raised by Hanks and sequence of actions. In order to keep our notation as McDermott, both formal and philosophical, have been manageable as possible, we will denote the fact that some discussed at substantial length elsewhere.
    [Show full text]
  • The Situation Calculus and the Frame Problem
    The Situation Calculus and the Frame Problem Using Theorem Proving to Generate Plans Literature z Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice, section 12.2. Elsevier/Morgan Kaufmann, 2004. z Murray Shanahan. Solving the Frame Problem, chapter 1. The MIT Press, 1997. z Chin-Liang Chang and Richard Char-Tung Lee. Symbolic Logic and Mechanical Theorem Proving, chapters 2 and 3. Academic Press, 1973. The Situation Calculus and the Frame Problem 2 1 Classical Planning z restricted state-transition system Σ=(S,A,γ) z planning problem P=(Σ,si,Sg) z Why study classical planning? • good for illustration purposes • algorithms that scale up reasonably well are known • extensions to more realistic models known z What are the main issues? • how to represent states and actions • how to perform the solution search The Situation Calculus and the Frame Problem 3 Planning as Theorem Proving z idea: • represent states and actions in first-order predicate logic • prove that there is a state s • that is reachable from the initial state and • in which the goal is satisfied. • extract plan from proof The Situation Calculus and the Frame Problem 4 2 Overview Propositional Logic z First-Order Predicate Logic z Representing Actions z The Frame Problem z Solving the Frame Problem The Situation Calculus and the Frame Problem 5 Propositions z proposition: a declarative sentence (or statement) that can either true or false z examples: • the robot is at location1 • the crane is holding a container z atomic propositions (atoms): • have no internal structure • notation: capital letters, e.g.
    [Show full text]
  • The Situation Calculus and the Frame Problem the Situation Calculus and the Frame Problem •Using Theorem Proving to Generate P
    The Situation Calculus and the Frame Problem Using Theorem Proving to Generate Plans The Situation Calculus and the Frame Problem •Using Theorem Proving to Generate Plans 1 Literature z Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice, section 12.2. Elsevier/Morgan Kaufmann, 2004. z Murray Shanahan. Solving the Frame Problem, chapter 1. The MIT Press, 1997. z Chin-Liang Chang and Richard Char-Tung Lee. Symbolic Logic and Mechanical Theorem Proving, chapters 2 and 3. Academic Press, 1973. The Situation Calculus and the Frame Problem 2 Literature •Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice, section 12.2. Elsevier/Morgan Kaufmann, 2004. •Murray Shanahan. Solving the Frame Problem, chapter 1. The MIT Press, 1997. •Chin-Liang Chang and Richard Char-Tung Lee. Symbolic Logic and Mechanical Theorem Proving, chapters 2 and 3. Academic Press, 1973. •for propositional and first-order logic 2 Classical Planning z restricted state-transition system Σ=(S,A,γ) z planning problem P=(Σ,si,Sg) z Why study classical planning? • good for illustration purposes • algorithms that scale up reasonably well are known • extensions to more realistic models known z What are the main issues? • how to represent states and actions • how to perform the solution search The Situation Calculus and the Frame Problem 3 Classical Planning •restricted state-transition system Σ=(S,A,γ) •finite, fully observable, deterministic, and static with restricted goals and implicit time •planning problem P=(Σ,si,Sg)
    [Show full text]
  • CS 343 Artificial Intelligence
    CS 343 Artificial Intelligence Gordon S. Novak Jr. Department of Computer Sciences University of Texas at Austin Austin, Texas 78712 (512) 471-9569 [email protected] http://www.cs.utexas.edu/users/novak Copyright c Gordon S. Novak Jr. 1 Artificial Intelligence as Science Intelligence should be placed in the context of biology: Intelligence connects perception to action to help an organism survive. Intelligence is computation in the service of life, just as metabolism is chemistry in the service of life. Intelligence does not imply perfect understanding; every intelligent being has limited perception, memory, and computation. Many points on the spectrum of intelligence-versus-cost are viable, from insects to humans. AI seeks to understand the computations required for intelligent behavior and to produce computer systems that exhibit intelligence. Aspects of intelligence studied by AI include perception, motor control, communication using human languages, reasoning, planning, learning, and memory. 2 Scientific Goals of AI AI seeks to understand the working of the mind in mechanistic terms, just as medicine seeks to understand the working of the body in mechanistic terms. The mind is what the brain does. { Marvin Minsky The strong AI position is that any aspect of human intelligence could, in principle, be mechanized. 3 A.I. as Engineering How can we make computer systems more intelligent? • Perception to get input directly from the real world. • Autonomy to perform tasks that currently require human operators without human intervention or monitoring. • Flexibility in dealing with variability in the environment. • Ease of use: computers that are able to understand what the user wants from limited instructions in natural languages.
    [Show full text]
  • 1988-Why Things Go Wrong: a Formal Theory of Causal Reasoning
    From: AAAI-88 Proceedings. Copyright ©1988, AAAI (www.aaai.org). All rights reserved. Why Things Go Wrong: A Formal Theory of Causal Reasoning Leora Morgenstern and Lynn Andrea Stein Department of Computer Science Brown University Box 1910, Providence, RI 02912 Abstract 19861 [Shoham, 19871, [Kautz, 19861, [Lifschitz, 19861 [Lif- schitz, 19871, [Haugh, 19871). All of these solutions, how- This paper presents a theory of generalized tem- ever, while adequate for the Yale Shooting Problem itself, poral reasoning. We focus on the related prob- handle either forward or backward projection incorrectly, lems of and/or work only within a very limited temporal ontol- 1. Temporal Projection-determining all the ogy. Thus, they cannot serve as the basis for a theory of facts true in a chronicle, given a partial de- generalized temporal reasoning. scription of that chronicle, and In this paper, we present a solution to the problems 2. Explanation-figuring out what went wrong of both forward and backward temporal projection, based if an unexpected outcome occurs. upon the concept that actions happen only if they have to happen. We then show how our model lends itself to a We present a non-monotonic temporal logic based very natural characterization of the concept of an adequate on the notion that actions only happen if they explanation for an unexpected outcome. are motivated. We demonstrate that this the- In the next section, we survey the solutions that have ory handles generalized temporal projection cor- been proposed to the YSP, and explain why they can- rectly, and in particular, solves the Yale Shooting not handle general temporal projection accurately.
    [Show full text]
  • Heuristics, Concepts, and Cognitive Architecture: Toward Understanding How the Mind Works
    Western University Scholarship@Western Electronic Thesis and Dissertation Repository 4-20-2011 12:00 AM Heuristics, Concepts, and Cognitive Architecture: Toward Understanding How The Mind Works Sheldon J. Chow The University of Western Ontario Supervisor Dr. Christopher Viger The University of Western Ontario Graduate Program in Philosophy A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Sheldon J. Chow 2011 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Philosophy of Mind Commons Recommended Citation Chow, Sheldon J., "Heuristics, Concepts, and Cognitive Architecture: Toward Understanding How The Mind Works" (2011). Electronic Thesis and Dissertation Repository. 128. https://ir.lib.uwo.ca/etd/128 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. HEURISTICS, CONCEPTS, AND COGNITIVE ARCHITECTURE: TOWARD UNDERSTANDING HOW THE MIND WORKS (Spine title: Heuristics, Concepts, and Cognitive Architecture) (Thesis format: Monograph) by Sheldon Chow Graduate Program in Philosophy A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy The School of Graduate and Postdoctoral Studies The University of Western Ontario London, Ontario, Canada c Sheldon Joseph Chow 2011 THE UNIVERSITY OF WESTERN
    [Show full text]
  • 1993-The Frame Problem and Knowledge-Producing Actions
    From: AAAI-93 Proceedings. Copyright © 1993, AAAI (www.aaai.org). All rights reserved. ame Proble Richard B. Scherl* and Hector J. Levesquet Department of Computer Science University of Toronto Toronto, Ontario Canada M5S 3A6 email: [email protected] [email protected] Abstract knowledge-producing actions, that is, actions whose ef- fects are to change a state of knowledge. This paper proposes a solution to the frame prob- A standard example of a knowledge-producing ac- lem for knowledge-producing actions. An example tion is that of reading a number on a piece of pa- of a knowledge-producing action is a sense opera- per. Consider the problem of dialing the combination tion performed by a robot to determine whether of a safe [McCarthy and Hayes, 1969; Moore, 1980; or not there is an object of a particular shape Moore, 19851. If an agent is at the same place as the within its grasp. The work is an extension of safe, and knows the combination of the safe, then he Reiter’s solution to the frame problem for ordi- can open the safe by performing the action of dialing nary actions and Moore’s work on knowledge and that combination. If an agent is at the same place action. The properties of our specification are as both the safe and a piece of paper and he knows that knowledge-producing actions do not affect that the combination of the safe is written on the pa, fluents other than the knowledge fluent, and ac- per, he can open the safe by first reading the piece of tions that are not knowledge-producing only affect paper, and then dialing that combination.
    [Show full text]
  • The Frame Problem, Then and Now
    The Frame Problem, Then and Now Vladimir Lifschitz University of Texas at Austin My collaboration with John McCarthy started in 1984. He was interested then in what he called the commonsense law of inertia. That idea is related to actions, such as moving an object to a different location, or, for instance, toggling a light switch. According to this law, whatever we know about the state of affairs before executing an action can be presumed, by default, to hold after the action as well. Formalizing this default would resolve the difficulty known in AI as the frame problem. Reasoning with defaults is nonmonotonic. John proposed a solution to the frame problem based on the method of nonmonotonic reasoning that he called circumscription [1]. And then something unpleasant happened. Two researchers from Yale University discovered that John's proposed solution was incorrect [2]. Their counterexample involved the actions of loading a gun and shooting, and it became known as the \Yale Shooting Scenario." Twenty years later their paper received the AAAI Classic Paper Award [3]. But that was not all. Automated reasoning is notoriously difficult, and the presence of defaults adds yet another level of complexity. Even assuming that the problem with Yale Shooting is resolved, was there any hope, one could ask, that the commonsense law of inertia would ever become part of usable software? So John's proposal seemed unsound and non-implementable. It also seemed unnecessary, because other researchers have proposed approaches to the frame problem that did not require nonmonotonic reasoning [4, 5, 6]. There were all indications that his project just wouldn't fly.
    [Show full text]
  • On the Frame Problem in Procedure Specifications1
    On the Frame Problem in Procedure Specifications1 Alex Borgida 2 Rutgers University John Mylopoulos 3 University of Toronto Raymond Reiter 3 University of Toronto and the Canadian Institute for Advanced Research Abstract We give examples of situations where formal specifications of procedures in the standard pre/postcondition style become lengthy, cumbersome and difficult to change, a problem which is particularly acute in the case of object-oriented specifications with inheritance. We identify the problem as the inability to express that a procedure changes only those things it has to, leaving everything else unmodified, and review some attempts at dealing with this “frame problem” in the Software Specification community. The second part of the paper adapts a recent proposal for a solution to the frame problem in Artificial Intelligence --- the notion of explanation closure axioms --- to provide an approach whereby one can state such conditions succinctly and modularly, with the added advantage of having the specifier be reminded of things that she may have omitted saying in procedure specifications. Since this approach is based on standard Predicate Logic, its semantics is relatively straight-forward. The paper also suggests an algorithm which generates syntactically the explanation closure axioms from the pre/postcondition specifications, provided they are written in a restricted language, and suggests a model theory supporting it. Keywords: formal specifications, formal specification languages, proof obligations, semantics of specification languages, inheritance. 1 This paper is a modified and extended version of the paper titled "...And Nothing Else Changes: The Frame Problem in Procedure Specifications" presented by the authors at the Fifteenth International Conference on Software Engineering, Baltimore, May 1993.
    [Show full text]