Black and Wilensky (1979) have made serious methodological errors in analyzing story grammars, and in the process they have committed additional errors in applying formal language theory. Our arguments involve clarifying certain aspects of knowledge representation crucial to a proper treatment of story understanding.Particular criticisms focus on the following shortcomings of their presentation: 1) an erroneous statement from formal language theory, 2) misapplication of formal language theory to story grammars, 3) unsubstantiated and doubtful analogies with English grammar, 4) various non (...) sequiturs concerning the generation of non‐stories, 5) a false claim based on the artificial distinction between syntax and semantics, and 6) misinterpretation of the role of story grammars in story understanding.We conclude by suggesting appropriate criteria for the evaluation of story grammars. (shrink)

When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual (...) mind" real? This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one. (shrink)

I argue that consciousness is an aspect of an agent's intelligence, hence of its ability to deal adaptively with the world. In particular, it allows for the possibility of noting and correcting the agent's errors, as actions performed by itself. This in turn requires a robust self-concept as part of the agent's world model; the appropriate notion of self here is a special one, allowing for a very strong kind of self-reference. It also requires the capability to come to see (...) that world model as residing in its belief base , while then representing the actual world as possibly different, i.e., forming a new world-model. This suggests particular computational mechanisms by which consciousness occurs, ones that conceivably could be discovered by neuroscientists, as well as built into artificial systems that may need such capabilities. Consciousness, then, is not an epiphenomenon at all, but rather a key part of the functional architecture of suitably intelligent agents, hence amenable to study as much as any other architectural feature. I also argue that ignorance of how subjective states could be essentially functional does not itself lend credibility to the view that such states are not essentially functional; the strong self-reference proposal here is one possible functional explanation of consciousness. (shrink)

In this paper we provide an account of the structural underpinnings of self-awareness. We offer both an abstract, logical account.

ABSTRACT Although much effort has been expended by researchers in trying to maintain a consistent belief base in formalizing commonsense reasoning, there is some evidence that the nature of commonsense reasoning itself brings inconsistencies with it. I will outline a number of sources of such inconsistencies, and discuss why they appear unavoidable. I will also suggest that, far from being a roadblock to effective commonsense, (detected) inconsistencies are often a reasoner's best guide to what to do next.

Artificial Intelligence, in press. Abstract: For some time we have been developing, and have had significant practical success with, a time-sensitive, contradiction-tolerant logical reasoning engine called the active logic machine (ALMA). The current paper details a semantics for a general version of the underlying logical formalism, active logic. Central to active logic are special rules controlling the inheritance of beliefs in general (and of beliefs about the current time in particular), very tight controls on what can be derived from direct (...) contradictions (P &￢P), and mechanisms allowing an agent to represent and reason about its own beliefs and past reasoning. Furthermore, inspired by the notion that until an agent notices that a set of beliefs is contradictory, that set seems consistent (and the agent therefore reasons with it as if it were consistent), we introduce an “apperception function” that represents an agent’s limited awareness of its own beliefs, and serves to modify inconsistent belief sets so as to yield consistent sets. Using these ideas, we introduce a new definition of logical consequence in the context of active logic, as well as a new definition of soundness such that, when reasoning with consistent premises, all classically sound rules remain sound in our new sense. However, not everything that is classically sound remains sound in our sense, for by classical definitions, all rules with contradictory premises are vacuously sound, whereas in active logic not everything follows from a contradiction. (shrink)

In this paper, we present a meta-cognitive approach for dropping and reconsidering intentions, wherein concurrent actions and results are allowed, in the framework of the time-sensitive and contradiction-tolerant active logic.

The current paper details a restricted semantics for active logic, a time-sensitive, contradictiontolerant logical reasoning formalism. Central to active logic are special rules controlling the inheritance of beliefs in general, and beliefs about the current time in particular, very tight controls on what can be derived from direct contradictions (P &¬P ), and mechanisms allowing an agent to represent and reason about its own beliefs and past reasoning. Using these ideas, we introduce a new definition of model and of logical (...) consequence, as well as a new definition of soundness such that, when reasoning with consistent premises, all classically sound rules are sound for active logic. However, not everything that is classically sound remains sound in our sense, for by classical definitions, all rules with contradictory premises are vacuously sound, whereas in active logic not everything follows from a contradiction. (shrink)

This commentary suggests an alternate definition for metacognition, as well as an alternate basis for the relation in representation. These together open the way for an understanding of mindreading that is significantly different from the one advocated by Carruthers.

The studies of mind and language have traditionally been linked to one another. Indeed, theories of reference have over time brought more and more mind into meaning. Here I argue that the links must be made far stronger still if we are to understand either. I offer some criticism of the causal-functionalist theories of reference on this ground, and present some ideas for improvements. The upshot will be that intentionality is largely internal and very real indeed, that it provides a (...) genuine distinction between systems that have it and ones that do not, and that it hinges on internal complexities of a specifiable sort, ones that are tied to the detection of error. What is meaning, what is it good for, and what is a naturalist account of how it works? In this paper. I will argue that first two of these questions should be answered primarily in terms of processes inside the mind/brain, contrary to most recent thinking on this. In the sequel paper, I will argue that a key aspect of this is the physical body of the meaning agent, and propose a way in which this might work and how an external notion of reference can be recaptured from internal processes. (shrink)

The process of rationally revising beliefs in the light of new information is a topic of great importance and long-standing interest in artificial intelligence. Moreover, significant progress has been made in understanding the philosophical, logical, and computational foundations of belief revision. However, very little research has been reported with respect to the revision of other mental states, most notably propositional attitudes such as desires and intentions. In this paper, we present a first attempt to formulate a general framework for understanding (...) the revision of mental states. We develop an abstract belief-desire-intention model of agents, and introduce a notion of rationality for this model. We then present a series of formal postulates characterizing the processes of adding beliefs, desires, and intentions, updating costs and values, and removing beliefs, desires, and intentions. We also investigate the computational complexity of several problems involving the abstract model and comment on algorithms for revision. (shrink)

ABSTRACT Certain problems in commonsense reasoning lend themselves to the use of non-standard formalisms which we call active logics. Among these are problems of objects misidentification. In this paper we describe some technical issues connected with automated inference in active logics, using particular object misidentification problems as illustrations. Control of exponential growth of inferences is a key issue. To control this growth attention is paid to a limited version of an inference rule for negative introspection. We also present some descriptive (...) statistics for comparison with earlier active-logic approaches. (shrink)

The studies of mind and language have traditionally been linked to one another. Indeed, theories of reference (meaning, intentionality, content) have over time brought more and more mind into meaning. Here I argue that the links must be made far stronger still if we are to understand either. I offer some criticism of the causal-functionalist theories of reference on this ground, and present some ideas for improvements. The upshot will be that intentionality is largely internal and very real indeed, that (...) it provides a genuine distinction between systems that have it and ones that do not, and that it hinges on internal complexities of a specifiable sort, ones that are tied to the detection of error. What is meaning, what is it good for, and what is a naturalist (non-intentional) account of how it works? In this paper (part I of a two-part work). I will argue that first two of these questions should be answered primarily in terms of processes inside the mind/brain, contrary to most recent thinking on this. In the sequel paper (part II), I will argue that a key aspect of this is the physical body of the meaning agent, and propose a way in which this might work and how an external notion of reference can be recaptured from internal processes. (shrink)

Bojadziev has taken issue with my distinction between strong and weak self-reference, in saying that it is reference in general and not simply self-reference, that either is strong or weak. I agree completely. Here I clarify how I intend those notions and why I think that the strong case of self-reference is worthy of special attention. In short, I argue that all forms of referring involve a kind of self-referring.

Abstract: When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don’t even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our work on the Meta-Cognitive Loop, a domain-general approach to giving artificial systems the ability to notice, assess, (...) and repair problems. The goal is to make artificial systems more robust. (shrink)