The frameproblem 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 frameproblem 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 frameproblem 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 frameproblem is, in truth, a two-headed beast. The intra-context frameproblem challenges us to say how a purely mechanistic system may achieve appropriate, flexible and fluid action within a context. The inter-context frameproblem 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 frameproblem may be neutralized by systems of special-purpose adaptive couplings, while the inter-context frameproblem 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 frameproblem. I close by responding to Dreyfus' concerns. (shrink)
It is shown that the Fodor's interpretation of the frameproblem is the central indication that his version of the Modularity Thesis is incompatible with computationalism. Since computationalism is far more plausible than this thesis, the latter should be rejected.
As many philosophers agree, the frameproblem 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 frameproblem/relevance problem. As we will argue in this paper, Non-Axiomatic Reasoning System (NARS) can handle the frameproblem in a more proper manner, because the resulting solution seriously takes epistemic constraints on cognition as a fundamental theoretical principle. (shrink)
For many of the authors in this volume, this is the second attempt to explore what McCarthy and Hayes (1969) ﬁrst called the “FrameProblem”. 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_ FrameProblem), (...) 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 FrameProblem 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)
This paper investigates connectionism's potential to solve the frameproblem. The frameproblem 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 frameproblem 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 frameproblem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science. (shrink)
The frameproblem is widely reputed among philosophers to be one of the deepest and most difficult problems of cognitive science. This paper discusses three recent attempts to display this problem: Dennett's problem of ignoring obviously irrelevant knowledge, Haugeland's problem of efficiently keeping track of salient side effects, and Fodor's problem of avoiding the use of kooky concepts. In a negative vein, it is argued that these problems bear nothing but a superficial similarity to (...) the frameproblem of AI, so that they do not provide reasons to disparage standard attempts to solve it. More positively, it is argued that these problems are easily solved by slight variations on familiar AI themes. Finally, some discussion is devoted to more difficult problems confronting AI. (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 frameproblem 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)
The frameproblem 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 frameproblem 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 frameproblem than are alternative theories of belief, (...) most notably, the “map” theory of belief. (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 frameproblem; it is sometimes called the relevance problem. In this paper we describe the frameproblem 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 frameproblem 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 frameproblem 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 frameproblem has become a difficult task. To get a better idea of what the frameproblem 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 frameproblem; I show that these proposals misunderstand, and fail to address, an important aspect of the frameproblem. Finally, I argue that though human cognition does not solve the frameproblem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)
Chiappe and Kukla argue that relevance theory fails to solve the frameproblem as defined by Fodor. They are right. They are wrong, however, to take Fodors frameproblem too seriously. Fodors concerns, on the other hand, even though they are wrongly framed, are worth addressing. We argue that Relevance thoery helps address them.
I analyze the frameproblem 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 frameproblem 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 frameproblem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frameproblem and the many other problems for artificial epistemology. I present the solution to the frameproblem: 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)
While we agree that the frameproblem, 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 frameproblem to be avoided. (shrink)
The frameproblem 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 frameproblem is given; (2) The frameproblem is distinguished from related problems; (3) The main strategies for dealing with the frameproblem are outlined; (4) A difference between commonsense reasoning and prediction using a scientific theory is argued for; (...) (5) Some implications for the.. (shrink)
From its humble origins labeling a technical annoyance for a particular AI formalism, the term "frameproblem" 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.
ABSTRACTUnlike human soldiers, autonomous weapons systems are unaffected by psychological factors that would cause them to act outside the chain of command. This is a compelling moral justification for their development and eventual deployment in war. To achieve this level of sophistication, the software that runs AWS will have to first solve two problems: the frameproblem and the representation problem. Solutions to these problems will inevitably involve complex software. Complex software will create security risks and will (...) make AWS critically vulnerable to hacking. I claim that the political and tactical consequences of hacked AWS far outweigh the purported advantages of AWS not being affected by psychological factors and always following orders. Therefore, one of the moral justifications for the deployment of AWS is undermined. (shrink)
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 “FrameProblem” in cognitive science viz the problem of explaining global abductive inference. More accurately, I shall argue that Kleinberg’s procedure suggests a new variety of “fast and frugal heuristic” (Gigerenzer and Todd (1999)) capable of pressing maximum utility from the vast bodies of information and associations commanded by the biological brain. The paper thus takes up the challenge laid down by Fodor ((1983)(Ms)). Fodor depicts the problem of global knowledge-based reason as the point source of many paradigmatic failings of contemporary computational theories of mind. These failings, Fodor goes on to argue, cannot be remedied by any simple appeal to alternative (e.g. connectionist) modes of encoding and processing. I shall show, however, that connectionist models can provide for one neurologically plausible incarnation of Kleinberg’s procedure. The paper ends by noting that current commercial applications increasingly confront the kinds of challenge (such as managing complexity and making efficient use of vast data-bases) initially posed to biological thought and reason. (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 frameproblem. (shrink)
The aim of this paper is to give a systematic account of the so-called “measurement problem” in the frame of the standard interpretation of quantum mechanics. It is argued that there is not one but five distinct formulations of this problem. Each of them depends on what is assumed to be a “satisfactory” description of the measurement process in the frame of the standard interpretation. Moreover, the paper points out that each of these formulations refers not (...) to a unique problem, but to a set of sub-problems. (shrink)
ABSTRACT: Brandom argues that functionalism must ultimately fail because it will not be able to explain how we can holistically update our beliefs solely in terms of abilities possessed by non-linguistic things. In this paper I respond to this argument by arguing that non-linguistic animals encounter and overcome an analogous sort of holistic updating problem. I will also try to demystify holism and de-intellectualize language use/reasoning.
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)