MultiLanguage systems (ML systems) are formal systems allowing the use of multiple distinct logical languages. In this paper we introduce a class of ML systems which use a hierarchy of first order languages, each language containing names for the language below, and propose them as an alternative to modal logics. The motivations of our proposal are technical, epistemological and implementational. From a technical point of view, we prove, among other things, that the set of theorems of the most common modal logics can be embedded (under the obvious bijective mapping between a modal and a first order language) into that of the corresponding ML systems. Moreover, we show that ML systems have properties not holding for modal logics and argue that these properties are justified by our intuitions. This claim is motivated by the study of how ML systems can be used in the representation of beliefs (more generally, propositional attitudes) and provability, two areas where modal logics have been extensively used. Finally, from an implementation point of view, we argue that ML systems resemble closely the current practice in the computer representation of propositional attitudes and metatheoretic theorem proving.

ion-Partitioned Evaluator (APE) architecture which has been tested in a simulated, single-agent, indoor navigation domain [SH90]. The APE architecture is composed of a number of concurrent, hierarchically abstract action control layers, each representing and reasoning about some particular aspect of the agent's task domain. Implemented as a parallel blackboard-based planner, the five layers --- sensor/motor, spatial, temporal, causal, and conventional (general knowledge) --- effectively partition the agent's data processing duties along a number of dimensions including temporal granularity, information/resource use, and functional abstraction. Perceptual information flows strictly from the agent sensors (connected to the sensor /motor level) toward the higher levels, while command or goal-achievement information flows strictly downward towards the agent's effectors (also connected to the sensor/motor level). Besides mechanisms for communicating with other layers, each layer in the AP...

: This paper considers student modelling from the point of view of the formal techniques that are involved. It attempts to provide a theoretical, computational basis for student modelling which is psychologically neutral and independent of applications. It is derived mainly from various areas of theoretical artificial intelligence. Because of the intrinsic difficulty of the student modelling problem, these links to AI are often merely pointed out and not pursued in depth. Contents 1. Introduction 2. Foundations 3. An example 4. Initialising the student model 4.1 Explicit questioning 4.2 Default assumptions 5. Updating the student model 5.1 Diagnosis 5.1.1 Reconstruction 5.1.2 Cognitive diagnosis 5.1.3 Generative mechanisms 5.2 Revising beliefs 5.2.1 Discarding beliefs 5.2.2 Creating beliefs through reasoning 5.2.3 Limited reasoning 5.2.4 Meta-reasoning 5.2.5 Non-monotonic reasoning 5.2.6 Creating beliefs through learning 5.3 Beyond belief 5.3.1 Belief structures 5.3.2 Viewpoints 5.3....

The goal of this paper is to provide a formalization of monotonic belief and belief about belief in a multiagent environment. We distinguish between ideal beliefs, i.e., those beliefs which satisfy certain "idealized" properties which are unlikely to be possessed by real agents, and real beliefs. Our formalization is based on a set-theoretic specification of beliefs and, then, on the definition of the appropriate constructors which present the sets identified. This allows us to provide a uniform and taxonomic characterization of the possible ways in which ideal and real beliefs can arise. We provide intuitions about the conceptual importance of the cases analyzed by proving and discussing some equivalence results with some important modal systems modeling various forms of (non) logical omniscience. 1 Introduction We are interested in the formalization of monotonic belief and belief about belief in a multiagent environment. Here, we restrict ourselves to the propositional case. We dis...

The propositional content of a reference is the proposition attributing to the referent the properties that correspond to the nouns and modifiers in the reference (for example, the propositional content of `Mary' is that the referent is named `Mary'). During language comprehension, the hearer or reader must determine the set of beliefs with respect to which the propositional content of a reference is to be understood. In the prototypical case, this set consists of the propositions that she believes that the speaker or writer believes that she and the speaker or writer mutually believe. This paper identifies two contexts in which the propositional content of a specific reference is not understood with respect to this set--- subjective and objective sentences in third-person fictional narrative text---and identifies some implications of this for understanding specific references in these contexts. 1 Introduction Specific references are references to particular entities, for example, `a...

Intelligent Student Systems are a class of Intelligent Learning Environments that place the learner in the role of a tutor rather than a student. In an analogy with the educational practice of peer tutoring users learn by teaching the computer -- inverting the predominant `computer as tutor' metaphor. Intelligent Student Systems emphasize the learner's viewpoint in educational interactions in preference to the system's conception of the domain. These systems are considered to be less complex than Intelligent Tutoring Systems and to have the potential to generate novel human-computer educational interactions. Viewpoints also have an integral part in knowledge representation in Intelligent Learning Environments and they are utilised in the design and implementation of an Intelligent Student System in economics. Testing of the system produced insights into the future application of Intelligent Student Systems.

ViewGen, an algorithm and program for belief ascription, represents the beliefs of agents as explicit, partitioned proposition-sets known as environments. A way of extending View-Gen to the interpretation of metaphor, and in particular to the comprehension of metaphor within the belief spaces of particular agents, has been described elsewhere. The paper reports the further refinement and recent implementation of this approach, as well as summarizing the argument for the claim that ordinary non-metaphorical belief ascription and the transfer of information in metaphors can both be seen as different manifestations of a single environment-amalgamation process, one in which explicitly metaphorical amalgamations are triggered by "preference breaking " in the sentence being processed. This requires a consideration of the scoping of metaphor with respect to belief contexts, analogous to the scoping of quantification and definite descriptions with respect to such contexts. As a topic of ongoing and future work, the issue of mixed metaphor, of two distinct types, is briefly addressed. 1 ViewGen: The Basic Belief Engine A computational model of belief ascription is described in detail elsewhere [Wilks and Bien, 1979, 1983] [Ballim, 1987] [Wilks and Ballim, 1987] [Ballim and Wilks, in press] and is embodied in a prolog program called View-Gen. The basic algorithm of this model uses the notion of default reasoning to ascribe beliefs to other agents unless there is evidence to prevent the ascription. Perrault [1987, 1990] and Cohen and Levesque [1985] have also recently explored a belief and speech act logic based on a single explicit default axiom. As our previous work has shown for some years, the default ascription is basically correct, but the phenomena are more complex than are normally captured by an axiomatic approach. ViewGen also avoids certain counter-intuitive assumptions, such as the non-persistence of ignorance about any given proposition p [Perrault, 1990]. Also such systems

This paper discusses the extension of ViewGen, a program for belief ascription, to the area of inten-sional object identification with applications to battle environments, and its combination in a overall sys-tem with MGR, a Model-Generative Reasoning system, and PREMO a semantics-based parser for robust parsing of noisy message data. ViewGen represents the beliefs of agents as explicit, partitioned proposition-sets known as environ-ments. Environments are convenient, even essential, for addressing important pragmatic issues of reason-ing. The paper concentrates on showing that the transfer of information in intensional object identification and belief ascription itself can both be seen as different manifestations of a single environment-amalgamation process. The entities we shall be concerned with will be ones, for example, the system itself believes to be separate entities while it is computing the beliefs and reasoning of a hos-tile agent that believes them to be the same entity (e.g. we believe enemy radar shows two of our ships to be the same ship, or vice-versa. The KAL disaster should bring the right kind of scenario to mind). The representational issue we address is how to represent that fictional single entity in the belief space of the other agent, and what content it should have given that it is an amalgamation of two real entities. A major feature of the paper is our work on embedding within the ViewGen belief-and-point-of-view system the knowledge representation system of our MGR reasoner, and then bringing together the multiple viewpoints offered by ViewGen with the multiple representations of MGR. The fusing of these techniques, we believe, offers a very strong system for extracting message gists from texts and reasoning about them.

We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an F-measure of 64%, a relative reduction in F-measure error of 21 % over not using syntactic features. 1

this paper, motivates McCarthy's attitude (and that taken by us here) of using the notions defined for common sense for intelligence and vice versa. It is important to notice that the two notions are not collapsed. The main difference seems that intelligence requires that the associated capabilities be "good enough" to achieve a goal. Thus the first requirement for having common sense is to know about the surrounding environment, while being intelligent requires that such a representation be adequate (property 1). The second requirement for having common sense is being able to derive consequences from what is known, most often without a strong reasoning capability and possibly without involving any intelligence [2]. Intelligence requires instead the capability of answering "a wide variety of questions," possibly on difficult topics, e.g. mathematics and people's mental processes (property 2). Finally, the third requirement for common sense is the possibility to provide data to the advice taker without knowing of its actual internal state and functioning. An intelligent entity can instead actively use its capabilities to get information from the outside and to act as needed to satisfy a goal (property 3). However, as far as I know, these differences have not played any role in McCarthy's research.