One of the central questions of legal philosophy concerns the division of power between the judicial and legislative branches of government: where is the border between a judge's power to decide cases by applying the law, and the legislature's power to create law? How should it be decided whether or not a judge has exceeded the limits of his discretion? The Pleadings Game is a normative formalization and computational model of legal reasoning and argumentation intended to help answer these questions. In the prominent theory of H.L.A. Hart, judicial discretion is limited by the literal meaning of legislation. To use the standard example, if a law prohibits vehicles from a park, according to Hart's theory a judge would not have discretion to permit a military tank, even if intended to be used as a war memorial. The Pleadings Game is based on another approach, Robert Alexy's discourse theory of legal argumentation. Alexy views legal reasoning as a rule governed language game, where the r...

A theoretical framework for grounding language is introduced that provides a computational path from sensing and motor action to words and speech acts. The approach combines concepts from semiotics and schema theory to develop a holistic approach to linguistic meaning. Schemas serve as structured beliefs that are grounded in an agent’s physical environment through a causal-predictive cycle of action and perception. Words and basic speech acts are interpreted in terms of grounded schemas. The framework reflects lessons learned from implementations of several language processing robots. It provides a basis for the analysis and design of situated, multimodal communication systems that straddle symbolic and non-symbolic realms.

Children learn a variety of verbs for hand actions starting in their second year of life. The semantic distinctions can be subtle, and they vary across languages, yet they are learned quickly. Howis this possible? This dissertation explores the hypothesis that to explain the acquisition and use of action verbs, motor control must be taken into account. It presents a model of embodied semantics|based on the principles of neural computation in general and on the human motor system in particular|which takes a set of labelled actions and learns both to label novel actions and to obey verbal commands. Akey feature of the model is the executing schema, anactivecontroller mechanism which, by actually driving behavior, allows the model to carry out verbal commands. A hard-wired mechanism links the activity of executing schemas to a set of linguistically important features including hand posture, joint motions, force, aspect and goals. The feature set is relatively small and is xed, helping to make learning tractable. Moreover, the use of traditional feature structures facilitates the use of model merging, a Bayesian probabilistic learning algorithm which rapidly learns plausible word meanings, automatically determines an appropriate number of senses for each verb, and can plausibly be mapped to a connectionist recruitment

This paper surveys research in developing computational models for integrating linguistic and visual information. It begins with a discussion of systems which have been actually implemented and continues with computationally motivated theories of human cognition. Since existing research spans several disciplines (e.g., natural language understanding, computer vision, knowledge representation), as well as several application areas, an important contribution of this paper is to categorize existing research based on inputs and objectives. Finally, some key issues related to integrating information from two such diverse sources are outlined and related to existing research. Throughout, the key issue addressed is the correspondence problem, namely how to associate visual events with words and vice versa. 1 Introduction Much has been said about the necessity of linking language and vision in order for a system to exhibit intelligent behaviour [Win73, Wal81]. A complete natural-language und...

In this paper we present one aspect of our presents a clear case of where general as well as specific research on machine translation (MT): defining the re- real world knowledge should assist in eliminating inap- lation between the interlingua (IL) and a knowledge representation (KR) within an MT system. Our interest lies in the translation of natural language (NL) sentences where the "message" contains a spatial relation in particular, where the sentence conveys information about the location or path of physical entities in the real, physical world. We explore several arguments for clarifying the source of constraints on the particular IL structures needed to translate these sentences.

For years, researchers have argued that Complex Problem Solving (CPS) is plagued with methodological problems. The interest of this research paradigm, a hybrid between field studies and experimental ones, is tied to the success of methodological advances that enable performance to be analyzed.

1 Language in the World How does language relate to the non-linguistic world? If an agent is able to communicate linguistically and is also able to directly perceive and/or act on the world, how do perception, action, and language interact with and influence each other? Such questions are surely amongst the most important in Cognitive Science and Artificial Intelligence (AI). Language, after all, is a central aspect of the human mind – indeed it may be what distinguishes us from other species. There is sometimes a tendency in the academic world to study language in isolation, as a formal system with rules for well-constructed sentences; or to focus on how language relates to formal notations such as symbolic logic. But language did not evolve as an isolated system or as a way of communicating symbolic logic; it presumably evolved as a mechanism for exchanging information about the world, ultimately providing the medium for cultural transmission across generations. Motivated by these observations, the goal of this special issue is to bring together research in AI that focuses on relating language to the physical world. Language is of course also used to communicate about non-physical referents, but the ubiquity of physical metaphor in language [21] suggests that grounding in the physical world provides the foundations of semantics.

This chapter investigates the computational consequences of a broadly Gricean view of language use as intentional activity. In this view, dialogue rests on coordinated reasoning about communicative intentions. The speaker produces each utterance by formulating a suitable communicative intention. The hearer understands it by recognizing the communicative intention behind it. When this coordination is successful, interlocutors succeed in considering the same intentions— that is, the same representations of utterance meaning—as the dialogue proceeds. In this paper, I emphasize that these intentions can be formalized; we can provide abstract but systematic representations that spell out what a speaker is trying to do with an utterance. Such representations describe utterances simultaneously as the product of our knowledge of grammar and as actions chosen for a reason. In particular, they must characterize the speaker’s utterance in grammatical terms, provide the links to the context that the grammar requires, and so arrive at a contribution that the speaker aims to achieve. Because I have implemented this formalism, we can regard it as a possible analysis of conversational processes at the level of computational theory. Nevertheless, this analysis leaves open what the nature of the biological computation involved in inference to intentions is, and what regularities in language use support this computation.

Most existing natural language interfaces to databases (Nlidbs) were designed to be used with “snapshot ” database systems, that provide very limited facilities for manipulating time-dependent data. Consequently, most Nlidbs also provide very limited support for the notion of time. In particular, they were designed to answer questions that refer mainly to the present, and do not support adequately the mechanisms that natural language uses to express time. The database community is becoming increasingly interested in temporal database systems. These are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. When interfacing to temporal databases, it becomes crucial for Nlidbs to interpret correctly temporal linguistic mechanisms (verb tenses, temporal adverbials, temporal subordinate clauses, etc.) I argue that previous approaches to natural language interfaces for temporal databases (Nlitdbs) are problematic, mainly because they ignore important time-related linguistic phenomena, and/or they assume idiosyncratic temporal database systems. This

In this paper, we claim that the disambiguation of referring expressions in discourse can be formulated in terms that automated reasoners can address. Specifically, we show that consistency, informativity and minimality are criteria which can be (i) implemented using automated reasoning tools and (ii) used to disambiguate noun-noun compounds, metonymy and definite descriptions.