The Frame Problem

About three decades ago, AI theory underwent a sharp turn as a consequence of criticism that pointed out the problem of externalism in the cognitivist position. Hubert Dreyfus, undoubtedly the main exponent of this criticism, opened the possibility of a Heideggerian reading using the frame problem to bring to light obscurities that otherwise would have been very difficult to detect. However, the question still remains of whether or not Heidegger’s philosophy can serve as the source of a positive contribution to (...) AI. In this paper, we argue that in the small measure in which such a task has been attempted, its orientation has been hampered by the omission of what, for Heidegger, was the central issue to be pondered: the question for being and the ontological difference. To propose a possible direction in which AI can be headed as a consequence of this novel perspective, we undertake a brief and schematic review of two published projects suggesting that they both manage to avoid the frame problem and also bear this Heideggerian outlook. (shrink)

I put forward an externalist theory of social understanding. On this view, psychological sense making takes place in environments that contain both agent and interpreter. The spatial structure of such environments is social, in the sense that its occupants locate its objects by an exercise in triangulation relative to each of their standpoints. This triangulation is achieved in intersubjective interaction and gives rise to a triadic model of the social mind. This model can then be used to make sense of (...) others’ observed actions. Its possession plays a vital role in the development of the capacity for false belief reasoning. The view offers an integrated account of the development of social cognition from primary intersubjectivity to level-2 perspective taking. It incorporates insights from interactionism and mindreading theories of social cognition and thus offers a way out of the stalemate between defenders of the two views. Because psychological sense making is perspectival, the frame problem does not arise for social reasoners: the perspective they bring to bear on the action that is to be interpreted constrains the information they can select to make sense of what others do. (shrink)

We humans often respond effectively when faced with novel circumstances. This is because we are able to predict how particular alterations to the world will play out. Philosophers, psychologists, and computational modelers have long favored an account of this process that takes its inspiration from the truth-preserving powers of formal deduction techniques. There is, however, an alternative hypothesis that is better able to account for the human capacity to predict the consequences worldly alterations. This alternative takes its inspiration from the (...) powers of truth preservation exhibited by scale models and leads to a determinate computational solution to the frame problem. (shrink)

Jerry Fodor deemed informational encapsulation ‘the essence’ of a system’s modularity and argued that human perceptual processing comprises modular systems, thus construed. Nowadays, his conclusion is widely challenged. Often, this is because experimental work is seen to somehow demonstrate the cognitive penetrability of perceptual processing, where this is assumed to conflict with the informational encapsulation of perceptual systems. Here, I deny the conflict, proposing that cognitive penetration need not have any straightforward bearing on the conjecture that perceptual processing is composed (...) of nothing but informationally encapsulated modules, the conjecture that each and every perceptual computation is performed by an informationally encapsulated module, and the consequences perceptual encapsulation was traditionally expected to have for a perception-cognition border, the epistemology of perception and cognitive science. With these points in view, I propose that particularly plausible cases of cognitive penetration would actually seem to evince the encapsulation of perceptual systems rather than refute/problematize this conjecture. (shrink)

The open-domain Frame Problem is the problem of determining what features of an open task environment need to be updated following an action. Here we prove that the open-domain Frame Problem is equivalent to the Halting Problem and is therefore undecidable. We discuss two other open-domain problems closely related to the Frame Problem, the system identification problem and the symbol-grounding problem, and show that they are similarly undecidable. We then reformulate the Frame Problem as a quantum decision problem, and show (...) that it is undecidable by any finite quantum computer. (shrink)

The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...) that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)

Attempts to engineer a generally intelligent artificial agent have yet to meet with success, largely due to the (intercontext) frame problem. Given that humans are able to solve this problem on a daily basis, one strategy for making progress in AI is to look for disanalogies between humans and computers that might account for the difference. It has become popular to appeal to the emotions as the means by which the frame problem is solved in human agents. The purpose of (...) this paper is to evaluate the tenability of this proposal, with a primary focus on Dylan Evans’ search hypothesis and Antonio Damasio’s somatic marker hypothesis. I will argue that while the emotions plausibly help solve the intracontext frame problem, they do not function to solve or help solve the intercontext frame problem, as they are themselves subject to contextual variability. (shrink)

This is an expository article about the solution to the frame problem proposed 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 applications.

The frame problem was originally a problem for Artificial Intelligence, but philosophers have interpreted it as an epistemological problem for human cognition. As a result of this reinterpretation, however, specifying the frame problem has become a difficult task. To get a better idea of what the frame problem is, how it gives rise to more general problems of relevance, and how deep these problems run, I expound six guises of the frame problem. I then assess some proposed heuristic solutions to (...) the frame problem; I show that these proposals misunderstand, and fail to address, an important aspect of the frame problem. Finally, I argue that though human cognition does not solve the frame problem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)

The field of Artificial Intelligence has been around for over 60 years now. Soon after its inception, the founding fathers predicted that within a few years an intelligent machine would be built. That prediction failed miserably. Not only hasn’t an intelligent machine been built, but we are not much closer to building one than we were some 50 years ago. Many reasons have been given for this failure, but one theme has been dominant since its advent in 1969: The Frame (...) Problem. What looked initially like an innocuous problem in logic, turned out to be a much broader and harder problem of holism and relevance in commonsense reasoning. Despite an enormous literature on the topic, there is still disagreement not only on whether the problem has been solved, but even what exactly the problem is. In this paper we provide a formal description of the initial problem, the early attempts at a solution, and its ramification both in AI as well as philosophy. (shrink)

Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe (...) recent empirical work on analogical cognition as well as model applications to philosophical topics. Topics to be discussed include the epistemological distinction between implicit knowledge and explicit knowledge, the debate between empiricists and nativists, the frame problem, expertise, creativity and autism, cognitive architecture, and relational knowledge. Particular attention is given to Dedre Gentner and colleague’s structure-mapping theory – the most developed and widely accepted model of analogical cognition. (shrink)

The problem of concept representation is relevant for many sub-fields of cognitive research, including psychology and philosophy, as well as artificial intelligence. In particular, in recent years it has received a great deal of attention within the field of knowledge representation, due to its relevance for both knowledge engineering as well as ontology-based technologies. However, the notion of a concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs (...) is that the notion of a concept is, to some extent, heterogeneous, and encompasses different cognitive phenomena. This results in a strain between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In some ways artificial intelligence research shows traces of this situation. In this paper, we propose an analysis of this current state of affairs. Since it is our opinion that a mature methodology with which to approach knowledge representation and knowledge engineering should also take advantage of the empirical results of cognitive psychology concerning human abilities, we outline some proposals for concept representation in formal ontologies, which take into account suggestions from psychological research. Our basic assumption is that knowledge representation systems whose design takes into account evidence from experimental psychology may therefore give better results in many applications. (shrink)

As many philosophers agree, the frame problem is concerned with how an agent may efficiently filter out irrelevant information in the process of problem-solving. Hence, how to solve this problem hinges on how to properly handle semantic relevance in cognitive modeling, which is an area of cognitive science that deals with simulating human's cognitive processes in a computerized model. By "semantic relevance", we mean certain inferential relations among acquired beliefs which may facilitate information retrieval and practical reasoning under certain epistemic (...) constraints, e. g., the insufficiency of knowledge, the limitation of time budget, etc. However, traditional approaches to relevance—as for example, relevance logic, the Bayesian approach, as well as Description Logic—have failed to do justice to the foregoing constraints, and in this sense, they are not proper tools for solving the frame problem/relevance problem. As we will argue in this paper, Non-Axiomatic Reasoning System (NARS) can handle the frame problem in a more proper manner, because the resulting solution seriously takes epistemic constraints on cognition as a fundamental theoretical principle. (shrink)

Within cognitive science, mental processing is often construed as computation over mental representations—i.e., as the manipulation and transformation of mental representations in accordance with rules of the kind expressible in the form of a computer program. This foundational approach has encountered a long-standing, persistently recalcitrant, problem often called the frame problem; it is sometimes called the relevance problem. In this paper we describe the frame problem and certain of its apparent morals concerning human cognition, and we argue that these morals (...) have significant import regarding both the nature of moral normativity and the human capacity for mastering moral normativity. The morals of the frame problem bode well, we argue, for the claim that moral normativity is not fully systematizable by exceptionless general principles, and for the correlative claim that such systematizability is not required in order for humans to master moral normativity. (shrink)

The frame problem is the difficulty of explaining how non-magical systems think and act in ways that are adaptively sensitive to context-dependent relevance. Influenced centrally by Heideggerian phenomenology, Hubert Dreyfus has argued that the frame problem is, in part, a consequence of the assumption (made by mainstream cognitive science and artificial intelligence) that intelligent behaviour is representation-guided behaviour. Dreyfus' Heideggerian analysis suggests that the frame problem dissolves if we reject representationalism about intelligence and recognize that human agents realize the property (...) of thrownness (the property of being always already embedded in a context). I argue that this positive proposal is incomplete until we understand exactly how the properties in question may be instantiated in machines like us. So, working within a broadly Heideggerian conceptual framework, I pursue the character of a representation-shunning thrown machine. As part of this analysis, I suggest that the frame problem is, in truth, a two-headed beast. The intra-context frame problem challenges us to say how a purely mechanistic system may achieve appropriate, flexible and fluid action within a context. The inter-context frame problem challenges us to say how a purely mechanistic system may achieve appropriate, flexible and fluid action in worlds in which adaptation to new contexts is open-ended and in which the number of potential contexts is indeterminate. Drawing on the field of situated robotics, I suggest that the intra-context frame problem may be neutralized by systems of special-purpose adaptive couplings, while the inter-context frame problem may be neutralized by systems that exhibit the phenomenon of continuous reciprocal causation. I also defend the view that while continuous reciprocal causation is in conflict with representational explanation, special-purpose adaptive coupling, as well as its associated agential phenomenology, may feature representations. My proposal has been criticized recently by Dreyfus, who accuses me of propagating a cognitivist misreading of Heidegger, one that, because it maintains a role for representation, leads me seriously astray in my handling of the frame problem. I close by responding to Dreyfus' concerns. (shrink)

The frame problem is the problem of how we selectively apply relevant knowledge to particular situations in order to generate practical solutions. Some philosophers have thought that the frame problem can be used to rule out, or argue in favor of, a particular theory of belief states. But this is a mistake. Sentential theories of belief are no better or worse off with respect to the frame problem than are alternative theories of belief, most notably, the “map” theory of belief.

This volume is a collection of papers that explore various areas of common interest between philosophy, computing, and cognition. The book illustrates the rich intrigue of this fascinating recent intellectual story. It begins by providing a new analysis of the ideas related to computer ethics, such as the role in information technology of the so-called moral mediators, the relationship between intelligent machines and warfare, and the new opportunities offered by telepresnece, for example in teaching and learning. The book also ties (...) together the concerns of epistemology and logic, showing, for example, the connections between computers, bio-robotics, and scientific research and between computational programs and scientific discovery. Important results coming from recent computational models of deduction, the dynamic nature of meaning, and the role of reasoning and learning in spatial, visual and exemplar-based compuational frameworks are also addressed. Some stimulating papers carefully study how the interplay between computing and philosophy has also shed new light on the role of rational acceptance in the logic of belief and on the status of old philosophical topics like embodiment and consciousness, the role of information and the problem of realism in the new digital world. Finally, a considerable part of the book addresses the role of intenal and external representations in scientific reasoning and creative inferences as well as the place of manipulation of objects and artifacts in human cognition. Taking these topics together this book describes an aspect of an emerging agenda which is likely to carry the interaction between philosophy, cognition and computing forward into the twenty-first century. The volume is based on the papers that were presented at the International European Conference Computing and Philosophy, E-CAP2004, Italy, held at the University of Paiva, Paiv, Italy in June 2004, chaired by Lorenzo Magnani. (shrink)

The frame problem is a problem in artificial intelligence that a number of philosophers have claimed has philosophical relevance. The structure of this paper is as follows: (1) An account of the frame problem is given; (2) The frame problem is distinguished from related problems; (3) The main strategies for dealing with the frame problem are outlined; (4) A difference between commonsense reasoning and prediction using a scientific theory is argued for; (5) Some implications for the..

Kleinberg (1999) describes a novel procedure for efficient search in a dense hyper-linked environment, such as the world wide web. The procedure exploits information implicit in the links between pages so as to identify patterns of connectivity indicative of “authorative sources”. At a more general level, the trick is to use this second-order link-structure information to rapidly and cheaply identify the knowledge- structures most likely to be relevant given a specific input. I shall argue that Kleinberg’s procedure is suggestive of (...) a new, viable, and neuroscientifically plausible solution to at least (one incarnation of) the so-called “Frame Problem” in cognitive science viz the problem of explaining global abductive inference. More accurately, I shall argue that. (shrink)

The theory-theory claims that the explanation and prediction of behavior works via the application of a theory, while the simulation theory claims that explanation works by putting ourselves in others' places and noting what we would do. On either account, in order to develop a prediction or explanation of another person's behavior, one first needs to have a characterization of that person's current or recent actions. Simulation requires that I have some grasp of the other person's behavior to project myself (...) upon; whereas theorizing requires a subject matter to theorize about. The frame problem shows that multiple, true characterizations are possible for any behavior or situation. However, only one or a few of these characterizations are relevant to explaining or predicting behavior. Since different characterizations of a behavior lead to different predictions or explanations, much of the work of interpersonal interpretation is done in the process of finding this characterization - that is, prior to either theorizing or simulating. Moreover, finding this characterization involves extensive knowledge of the physical, cultural, and social worlds of the persons involved. (shrink)

While we agree that the frame problem, as initially stated by McCarthy and Hayes (1969), is a problem that arises because of the use of representations, we do not accept the anti-representationalist position that the way around the problem is to eliminate representations. We believe that internal representations of the external world are a necessary, perhaps even a defining feature, of higher cognition. We explore the notion of dynamically created context-dependent representations that emerge from a continual interaction between working memory, (...) external input, and long-term memory. We claim that only this kind of representation, necessary for higher cognitive abilities such as counterfactualization, will allow the combinatorial explosion inherent in the frame problem to be avoided. (shrink)

Reasoning is an integral part of intelligent systems in fields like databases, logic programming, robotics, knowledge engineering, human/computer interfaces, programming environments, etc. In reality any such system has to cope with a changing world and its dynamics. Hence it is of great importance that reasoning must account for coping with change in order to be truly useful in practice. The book comprises several contributions to current ways of approaching this problem. On the one hand it surveys and synthesizes recent research (...) work, while on the other hand new research results are included. Among the topics treated are logics for reasoning about actions and planning, belief revision and the reconciliation of logically conflicting inputs, resolving of conflicts by merging of knowledge and issues in the evolution in object-oriented databases. The book is aimed at the researcher and advanced student active in this field. (shrink)

This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts can (...) be raised about the potential of distributed representations to allow large amounts of complexly structured information to be adequately encoded and processed. It is questionable whether connectionist models that are claimed to effectively represent structured information can be scaled up to a realistic extent. We conclude that the frame problem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science. (shrink)

I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by (...) Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers. (shrink)

From its humble origins labeling a technical annoyance for a particular AI formalism, the term "frame problem" has grown to cover issues confronting broader research programs in AI. In philosophy, the term has come to encompass allegedly fundamental, but merely superficially related, objections to computational models of mind in AI and beyond.

In 1969, John McCarthy and Pat Hayes uncovered a problem that has haunted the field of artificial intelligence ever since--the frame problem. The problem arises when logic is used to describe the effects of actions and events. Put simply, it is the problem of representing what remains unchanged as a result of an action or event. Many researchers in artificial intelligence believe that its solution is vital to the realization of the field's goals. Solving the Frame Problem presents the various (...) approaches to the frame problem that have been proposed over the years. The author presents the material chronologically--as an unfolding story rather than as a body of theory to be learned by rote. There are lessons to be learned even from the dead ends researchers have pursued, for they deepen our understanding of the issues surrounding the frame problem. In the book's concluding chapters, the author offers his own work on event calculus, which he claims comes very close to a complete solution to the frame problem. Artificial Intelligence series. (shrink)

Sperber and Wilson (1987) have criticised Fodor's (1983) pessimistic view about the possibility of a science of central systems. Fodor's pessimism stems from the holistic nature of central systems – people can access anything that they know when engaging in belief fixation. It is argued that Sperber and Wilsons theory of how relevance is realized during verbal comprehension fails to elucidate this crucial aspect of central processes. Their claims about how a context is selected are shown to presuppose the ability (...) to realize relevance. (shrink)

The chapters in this book have evolved from talks originally presented at The First International Workshop on Human and Machine Cognition.

Reasoning about change is a central issue in research on human and robot planning. We study an approach to reasoning about action and change in a dynamic logic setting and provide a solution to problems which are related to the Frame problem. Unlike most work on the frame problem the logic described in this paper is monotonic. It (implicitly) allows for the occurrence of actions of multiple agents by introducing non-stationary notions of waiting and test. The need to state a (...) large number of frame axioms is alleviated by introducing a concept of chronological preservation to dynamic logic. As a side effect, this concept permits the encoding of temporal properties in a natural way. We compare the relative merits of our approach and non-monotonic approaches as regards different aspects of the frame problem. Technically, we show that the resulting extended systems of propositional dynamic logic preserve (weak) completeness, finite model property and decidability. (shrink)

For many of the authors in this volume, this is the second attempt to explore what McCarthy and Hayes (1969) first called the “Frame Problem”. Since the first compendium (Pylyshyn, 1987), nicely summarized here by Ronald Loui, there have been several conferences and books on the topic. Their goals range from providing a clarification of the problem by breaking it down into subproblems (and sometimes declaring the hard subproblems to not be the_ real_ Frame Problem), to providing formal “solutions” to (...) certain aspects of the problem. But more often the message has been that the problem is not solvable except in a piecemeal way in special circumstances by some sort of heuristic approximations. It has sometimes also been said that solving the Frame Problem is not only an unachievable goal, but it is also an unnecessary one since_ humans_ do not solve it either; we simply get along as best we can and deal with the problem of planning in ways that, to use Dennett’s phrase, is “good enough for government work”. (shrink)

Chiappe and Kukla argue that relevance theory fails to solve the frame problem as defined by Fodor. They are right. They are wrong, however, to take Fodor’s frame problem too seriously. Fodor’s concerns, on the other hand, even though they are wrongly framed, are worth addressing. We argue that Relevance thoery helps address them.

While the notion of the mind as information-processor--a kind of computational system--is widely accepted, many scientists and philosophers have assumed that this account of cognition shows that the mind's operations are characterizable independent of their relationship to the external world. Existential Cognition challenges the internalist view of mind, arguing that intelligence, thought, and action cannot be understood in isolation, but only in interaction with the outside world. Arguing that the mind is essentially embedded in the external world, Ron McClamrock provides (...) a schema that allows cognitive scientists to address such long-standing problems in artificial intelligence as the "frame" problem and the issue of "bounded" rationality. Extending this schema to cover progress in other studies of behavior, including language, vision, and action, McClamrock reinterprets the importance of the organism/environment distinction. McClamrock also considers the broader philosophical question of the place of mind in the world, particularly with regard to questions of intentionality, subjectivity, and phenomenology. With implications for philosophy, cognitive and computer science, AI, and psychology, this book synthesizes state-of-the-art work in philosophy and cognitive science on how the mind interacts with the world to produce thoughts, ideas, and actions. (shrink)

I have discussed the frame problem and the Turing test at length, but I have not attempted to spell out what I think the implications of the frame problem ...