Standpoint Logic: A Logic for Handling Semantic Variability, with Applications to Forestry Information

It is widely accepted that most natural language expressions do not have precise universally agreed definitions that fix their meanings. Except in the case of certain technical terminology, humans use terms in a variety of ways that are adapted to different contexts and perspectives. Hence, even when conversation participants share the same vocabulary and agree on fundamental taxonomic relationships (such as subsumption and mutual exclusivity), their view on the specific meaning of terms may differ significantly. Moreover, even individuals themselves may not hold permanent points of view, but rather adopt different semantics depending on the particular features of the situation and what they wish to communicate. In this thesis, we analyse logical and representational aspects of the semantic variability of natural language terms. In particular, we aim to provide a formal language adequate for reasoning in settings where different agents may adopt particular standpoints or perspectives, thereby narrowing the semantic variability of the vague language predicates in different ways. For that purpose, we present standpoint logic, a framework for interpreting languages in the presence of semantic variability. We build on supervaluationist accounts of vagueness, which explain linguistic indeterminacy in terms of a collection of possible interpretations of the terms of the language (precisifications). This is extended by adding the notion of standpoint, which intuitively corresponds to a particular point of view on how to interpret vague terminology, and may be taken by a person or institution in a relevant context. A standpoint is modelled by sets of precisifications compatible with that point of view and does not need to be fully precise. In this way, standpoint logic allows one to articulate finely grained and structured stipulations of the varieties of interpretation that can be given to a vague concept or a set of related concepts and also provides means to express relationships between different systems of interpretation. After the specification of precisifications and standpoints and the consideration of the relevant notions of truth and validity, a multi-modal logic language for describing standpoints is presented. The language includes a modal operator for each standpoint, such that \standb{s}\phi means that a proposition $\phi$ is unequivocally true according to the standpoint $s$ --- i.e.\ $\phi$ is true at all precisifications compatible with $s$. We provide the logic with a Kripke semantics and examine the characteristics of its intended models. Furthermore, we prove the soundness, completeness and decidability of standpoint logic with an underlying propositional language, and show that the satisfiability problem is NP-complete. We subsequently illustrate how this language can be used to represent logical properties and connections between alternative partial models of a domain and different accounts of the semantics of terms. As proof of concept, we explore the application of our formal framework to the domain of forestry, and in particular, we focus on the semantic variability of `forest'. In this scenario, the problematic arising of the assignation of different meanings has been repeatedly reported in the literature, and it is especially relevant in the context of the unprecedented scale of publicly available geographic data, where information and databases, even when ostensibly linked to ontologies, may present substantial semantic variation, which obstructs interoperability and confounds knowledge exchange

Toward Understanding Human Expression in Human-Robot Interaction

Intelligent devices are quickly becoming necessities to support our activities during both work and play. We are already bound in a symbiotic relationship with these devices. An unfortunate effect of the pervasiveness of intelligent devices is the substantial investment of our time and effort to communicate intent. Even though our increasing reliance on these intelligent devices is inevitable, the limits of conventional methods for devices to perceive human expression hinders communication efficiency. These constraints restrict the usefulness of intelligent devices to support our activities. Our communication time and effort must be minimized to leverage the benefits of intelligent devices and seamlessly integrate them into society. Minimizing the time and effort needed to communicate our intent will allow us to concentrate on tasks in which we excel, including creative thought and problem solving. An intuitive method to minimize human communication effort with intelligent devices is to take advantage of our existing interpersonal communication experience. Recent advances in speech, hand gesture, and facial expression recognition provide alternate viable modes of communication that are more natural than conventional tactile interfaces. Use of natural human communication eliminates the need to adapt and invest time and effort using less intuitive techniques required for traditional keyboard and mouse based interfaces. Although the state of the art in natural but isolated modes of communication achieves impressive results, significant hurdles must be conquered before communication with devices in our daily lives will feel natural and effortless. Research has shown that combining information between multiple noise-prone modalities improves accuracy. Leveraging this complementary and redundant content will improve communication robustness and relax current unimodal limitations. This research presents and evaluates a novel multimodal framework to help reduce the total human effort and time required to communicate with intelligent devices. This reduction is realized by determining human intent using a knowledge-based architecture that combines and leverages conflicting information available across multiple natural communication modes and modalities. The effectiveness of this approach is demonstrated using dynamic hand gestures and simple facial expressions characterizing basic emotions. It is important to note that the framework is not restricted to these two forms of communication. The framework presented in this research provides the flexibility necessary to include additional or alternate modalities and channels of information in future research, including improving the robustness of speech understanding. The primary contributions of this research include the leveraging of conflicts in a closed-loop multimodal framework, explicit use of uncertainty in knowledge representation and reasoning across multiple modalities, and a flexible approach for leveraging domain specific knowledge to help understand multimodal human expression. Experiments using a manually defined knowledge base demonstrate an improved average accuracy of individual concepts and an improved average accuracy of overall intents when leveraging conflicts as compared to an open-loop approach

Personal calendar assistant that understands events

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 81-85).Calendar applications do not understand calendar entries. This limitation prevents them from offering the range of assistance that can be provided by a human personal assistant. Understanding calendar entries is a difficult problem because it involves integrating many types of knowledge: commonsense knowledge, about common events and the particular instances in the world, and user knowledge about the individual's preferences and goals. In this thesis, I present two models of event understanding: RoMULUS and JULIUS. ROMULUS addresses the problem of how missing information in a calendar entry can be filled in by having an event structure, goal knowledge, and past examples. This system is able to learn by observing the user, and constrains its inductive hypothesis by using knowledge about common goals specific to the event. Although this model is capable of representing some tasks, its structural assumptions limit the range of events that it can represent. JULIUS treats event understanding as a plan retrieval problem, and draws from the COMET plan library of 295 everyday plans to interpret the calendar entry. These plans are represented as a set of English activity phrases (i.e., "buy a cup of coffee"), and so the planning problem becomes a natural language understanding problem concerned with comprehending events. I show two techniques for retrieving plans: the first matches plans by their generalized predicate-argument structure, and the second retrieves plans by their goals. Goals are inferred by matching the plans against a database of 662 common goals, by computing the conceptual similarity between the goals and components of the plan. Combining the strengths of ROMULUS and JULIUS, I create a prototype of a personal assistant application, EVENTMINDER, that is able to recognize users' goals in order to propose relevant alternatives and provide useful recommendations.by Dustin Arthur Smith.S.M

Planning multisentential English text using communicative acts

The goal of this research is to develop explanation presentation mechanisms for knowledge based systems which enable them to define domain terminology and concepts, narrate events, elucidate plans, processes, or propositions and argue to support a claim or advocate action. This requires the development of devices which select, structure, order and then linguistically realize explanation content as coherent and cohesive English text. With the goal of identifying generic explanation presentation strategies, a wide range of naturally occurring texts were analyzed with respect to their communicative sttucture, function, content and intended effects on the reader. This motivated an integrated theory of communicative acts which characterizes text at the level of rhetorical acts (e.g., describe, define, narrate), illocutionary acts (e.g., inform, request), and locutionary acts (e.g., ask, command). Taken as a whole, the identified communicative acts characterize the structure, content and intended effects of four types of text: description, narration, exposition, argument. These text types have distinct effects such as getting the reader to know about entities, to know about events, to understand plans, processes, or propositions, or to believe propositions or want to perform actions. In addition to identifying the communicative function and effect of text at multiple levels of abstraction, this dissertation details a tripartite theory of focus of attention (discourse focus, temporal focus, and spatial focus) which constrains the planning and linguistic realization of text. To test the integrated theory of communicative acts and tripartite theory of focus of attention, a text generation system TEXPLAN (Textual EXplanation PLANner) was implemented that plans and linguistically realizes multisentential and multiparagraph explanations from knowledge based systems. The communicative acts identified during text analysis were formalized as over sixty compositional and (in some cases) recursive plan operators in the library of a hierarchical planner. Discourse, temporal, and spatial focus models were implemented to track and use attentional information to guide the organization and realization of text. Because the plan operators distinguish between the communicative function (e.g., argue for a proposition) and the expected effect (e.g., the reader believes the proposition) of communicative acts, the system is able to construct a discourse model of the structure and function of its textual responses as well as a user model of the expected effects of its responses on the reader's knowledge, beliefs, and desires. The system uses both the discourse model and user model to guide subsequent utterances. To test its generality, the system was interfaced to a variety of domain applications including a neuropsychological diagnosis system, a mission planning system, and a knowledge based mission simulator. The system produces descriptions, narrations, expositions, and arguments from these applications, thus exhibiting a broader range of rhetorical coverage than previous text generation systems

A perceptually based computational framework for the interpretation of spatial language

The goal of this work is to develop a semantic framework to underpin the development of natural language (NL) interfaces for 3 Dimensional (3-D) simulated environments. The thesis of this work is that the computational interpretation of language in such environments should be based on a framework that integrates a model of visual perception with a model of discourse. When interacting with a 3-D environment, users have two main goals the first is to move around in the simulated environment and the second is to manipulate objects in the environment. In order to interact with an object through language, users need to be able to refer to the object. There are many different types of referring expressions including definite descriptions, pronominals, demonstratives, one-anaphora, other-expressions, and locative-expressions Some of these expressions are anaphoric (e g , pronominals, oneanaphora, other-expressions). In order to computationally interpret these, it is necessary to develop, and implement, a discourse model. Interpreting locative expressions requires a semantic model for prepositions and a mechanism for selecting the user’s intended frame of reference. Finally, many of these expressions presuppose a visual context. In order to interpret them this context must be modelled and utilised. This thesis develops a perceptually grounded discourse-based computational model of reference resolution capable of handling anaphoric and locative expressions. There are three novel contributions in this framework a visual saliency algorithm, a semantic model for locative expressions containing projective prepositions, and a discourse model. The visual saliency algorithm grades the prominence of the objects in the user's view volume at each frame. This algorithm is based on the assumption that objects which are larger and more central to the user's view are more prominent than objects which are smaller or on the periphery of their view. The resulting saliency ratings for each frame are stored in a data structure linked to the NL system’s context model. This approach gives the system a visual memory that may be drawn upon in order to resolve references. The semantic model for locative expressions defines a computational algorithm for interpreting locatives that contain a projective preposition. Specifically, the prepositions in front of behind, to the right of, and to the left of. There are several novel components within this model. First, there is a procedure for handling the issue of frame of reference selection. Second, there is an algorithm for modelling the spatial templates of projective prepositions. This algonthm integrates a topological model with visual perceptual cues. This approach allows us to correctly define the regions described by projective preposition in the viewer-centred frame of reference, in situations that previous models (Yamada 1993, Gapp 1994a, Olivier et al 1994, Fuhr et al 1998) have found problematic. Thirdly, the abstraction used to represent the candidate trajectors of a locative expression ensures that each candidate is ascribed the highest rating possible. This approach guarantees that the candidate trajector that occupies the location with the highest applicability in the prepositions spatial template is selected as the locative’s referent. The context model extends the work of Salmon-Alt and Romary (2001) by integrating the perceptual information created by the visual saliency algonthm with a model of discourse. Moreover, the context model defines an interpretation process that provides an explicit account of how the visual and linguistic information sources are utilised when attributing a referent to a nominal expression. It is important to note that the context model provides the set of candidate referents and candidate trajectors for the locative expression interpretation algorithm. These are restncted to those objects that the user has seen. The thesis shows that visual salience provides a qualitative control in NL interpretation for 3-D simulated environments and captures interesting and significant effects such as graded judgments. Moreover, it provides an account for how object occlusion impacts on the semantics of projective prepositions that are canonically aligned with the front-back axis in the viewer-centred frame of reference

Intentions and Creative Insights: a Reinforcement Learning Study of Creative Exploration in Problem-Solving

Insight is perhaps the cognitive phenomenon most closely associated with creativity. People engaged in problem-solving sometimes experience a sudden transformation: they see the problem in a radically different manner, and simultaneously feel with great certainty that they have found the right solution. The change of problem representation is called "restructuring", and the affective changes associated with sudden progress are called the "Aha!" experience. Together, restructuring and the "Aha!" experience characterize insight. Reinforcement Learning is both a theory of biological learning and a subfield of machine learning. In its psychological and neuroscientific guise, it is used to model habit formation, and, increasingly, executive function. In its artificial intelligence guise, it is currently the favored paradigm for modeling agents interacting with an environment. Reinforcement learning, I argue, can serve as a model of insight: its foundation in learning coincides with the role of experience in insight problem-solving; its use of an explicit "value" provides the basis for the "Aha!" experience; and finally, in a hierarchical form, it can achieve a sudden change of representation resembling restructuring. An experiment helps confirm some parallels between reinforcement learning and insight. It shows how transfer from prior tasks results in considerably accelerated learning, and how the value function increase resembles the sense of progress corresponding to the "Aha!"-moment. However, a model of insight on the basis of hierarchical reinforcement learning did not display the expected "insightful" behavior. A second model of insight is presented, in which temporal abstraction is based on self-prediction: by predicting its own future decisions, an agent adjusts its course of action on the basis of unexpected events. This kind of temporal abstraction, I argue, corresponds to what we call "intentions", and offers a promising model for biological insight. It explains the "Aha!" experience as resulting from a temporal difference error, whereas restructuring results from an adjustment of the agent's internal state on the basis of either new information or a stochastic interpretation of stimuli. The model is called the actor-critic-intention (ACI) architecture. Finally, the relationship between intentions, insight, and creativity is extensively discussed in light of these models: other works in the philosophical and scientific literature are related to, and sometimes illuminated by the ACI architecture

Embodiment and Grammatical Structure: An Approach to the Relation of Experience, Assertion and Truth

In this thesis I address a concern in both existential phenomenology and embodied cognition, namely, the question of how ‘higher’ cognitive abilities such as language and judgements of truth relate to embodied experience. I suggest that although our words are grounded in experience, what makes this grounding and our higher abilities possible is grammatical structure. The opening chapter contrasts the ‘situated’ approach of embodied cognition and existential phenomenology with Cartesian methodological solipsism. The latter produces a series of dualisms, including that of language and meaning, whereas the former dissolves such dualisms. The second chapter adapts Merleau-Ponty’s arguments against the perceptual constancy hypothesis in order to undermine the dualism of grammar and meaning. This raises the question of what grammar is, which is addressed in the third chapter. I acknowledge the force of Chomsky’s observation that language is structure dependent and briefly introduce a minimal grammatical operation which might be the ‘spark which lit the intellectual forest fire’ (Clark: 2001, 151). Grammatical relations are argued to make possible the grounding of our symbols in chapters 4 and 5, which attempt to ground the categories of determiner and aspect in spatial deixis and embodied motor processes respectively. Chapter 6 ties the previous three together, arguing that we may understand a given lexeme as an object or as an event by subsuming it within a determiner phrase or aspectualising it respectively. I suggest that such modification of a word’s meaning is possible because determiners and aspect schematise, i.e. determine the temporal structure, of the lexeme. Chapter 7 uses this account to take up Heidegger’s claim that the relation between being and truth be cast in terms of temporality (2006, H349), though falls short of providing a complete account of the ‘origin of truth’. Chapter 8 concludes and notes further avenues of research