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An expert system is a computer program intended to embody the knowledge and ability of an expert in a certain domain. The ideas behind them and several examples have been described in other lectures in this symposium. Their performance in their specialized domains are often very impressive. Nevertheless, hardly any of them have certain common sense knowledge and ability possessed by any non-feeble-minded human. This lack makes them ``brittle''. By this is meant that they are difficult to extend beyond the scope originally contemplated by their designers, and they usually don't recognize their own limitations. Many important applications will require common sense abilities. The object of this lecture is to describe common sense abilities and the problems that require them. Common sense facts and methods are only very partially understo...
Aphasics, brain damaged patients with no language deficit, neurologically intact elderly subjects and university undergraduates matched pictures to sentences having compelling tacit implications (e.g. the sentence The fox grabs the hen strongly invites one to assume that the fox will eat the hen). All groups made, for the same sentences, qualitatively similar referential errors consisting in choosing a tacit implication picture. Two auxiliary experiments using the same target sentences in other interpretive situations permitted ruling out the possibility that these errors were due to the putative intrinsic semantic properties of the sentences, showing that the sentences which were most liable to elicit integrative error varied from task to task. These results are interpreted within the conceptual framework which posits that reliable di...
The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion.
Systems with multiple parallel goals (e.g. autonomous mobile robots) have a problem analogous to that of action selection in ethology. Architectures such as the subsumption architecture (Brooks) involve multiple sensing-and-acting agents within a single robot on its own if allowed. Which to give control at a given moment is normally regarded as a (difficult) problem of design. In a quest for a scheme where the agents decide for themselves in a sensible manner, I introduce a model where the agents are not only autonomous but are in full competition with each other for control of the robot. Interesting robots are ones where no agent achieves total victory, but rather a serires of compromises are reached. Having the agents operate by the reinforcement learning algorithm Q-learning (Watkins) allows the introduction of an algorithm called `...
The issue of context arises in assorted areas of Artificial Intelligence. Although its importance is realized by various researchers, there is not much work towards a useful formalization. In this paper, we will present a preliminary model (based on Situation Theory) and give examples to show the use of context in various fields, and the advantages gained by the acceptance of our proposal.
A common method of improving how well understood a theory is, is by comparing it to another theory which has been better developed. Radical interpretation is a theory which attempts to explain how communication has meaning. Radical interpretation is treated as another time dependent theory and compared to the time dependent theory of biological evolution. Several similarities and differences are uncovered. Biological evolution can be gradual or punctuated. Whether radical interpretation is gradual or punctuated depends on how the question is framed: on the coarse-grained time scale it proceeds gradually, but on the fine-grained time scale it proceeds by punctuated equilibria. Biological evolution proceeds by natural selection, the counterpart to this is the increase in both correspondence and coherence. Exaption, mutations, and spandre...
Wir stellen einen Ansatz zum Erwerb von Problemlösefertigkeiten vor, der auf dem im Bereich ``Automatisches Programmieren'' entwickelten Prinzip der Induktion rekursiver Programmschemata basiert. Dieser Ansatz ermöglicht es, drei Ebenen der Generalisierung zu modellieren: In einem ersten Schritt wird aufgrund konkreter Problemlöseerfahrungen ein bedingtes (initiales) Programm aufgebaut, das über die Problemlösungen für bereits explorierte Anfangszustände eines Problems generalisiert (learning by doing). Im zweiten Schritt wird aus diesem initialen Programm ein rekursives Programmschema inferiert. Dies entspricht einer Generalisierung über rekursiv aufzählbare Problemräume. In einem dritten Schritt kann von der konkreten Bedeutung der Operationssymbole eines Programmschemas abstrahiert werden. Die Struktur des rekursiven Programmschemas...
Ascribing mental qualities like beliefs, intentions and wants to a machine is sometimes correct if done conservatively and is sometimes necessary to express what is known about its state. We propose some new definitional tools for this: definitions relative to an approximate theory and second order structural definitions.
The success of set theory as a foundation for mathematics inspires its use in artificial intelligence, particularly in commonsense reasoning. In this survey, we briefly review classical set theory from an AI perspective, and then consider alternative set theories. Desirable properties of a possible commonsense set theory are investigated, treating different aspects like cumulative hierarchy, self-reference, cardinality, etc. Assorted examples from the ground-breaking research on the subject are also given.
This is a review of Formalizing Common Sense: Papers by John McCarthy, ed. by Vladimir Lifschitz, published by Ablex Publishing Corp. in 1990.
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