Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods

Psychotherapy change process research : realizing the promise

Change process research (CPR) is the study of the processes by which change occurs in psychotherapy, and is a necessary complement to randomized clinical trials and other forms of efficacy research. In this article I describe and evaluate of four types of CPR. The first three are basic designs and include quantitative Process-Outcome, qualitative Helpful Factors, and micro-analytic Sequential Process; the fourth, the Significant Events approach, refers to methods such as Task Analysis and Comprehensive Process Analysis that integrate the first three. The strengths and weaknesses of each design are described and summarized using both causal and practical criteria, as part of an overall argument for systematic methodological pluralism

Causal schema induction for knowledge discovery

Making sense of familiar yet new situations typically involves making generalizations about causal schemas, stories that help humans reason about event sequences. Reasoning about events includes identifying cause and effect relations shared across event instances, a process we refer to as causal schema induction. Statistical schema induction systems may leverage structural knowledge encoded in discourse or the causal graphs associated with event meaning, however resources to study such causal structure are few in number and limited in size. In this work, we investigate how to apply schema induction models to the task of knowledge discovery for enhanced search of English-language news texts. To tackle the problem of data scarcity, we present Torquestra, a manually curated dataset of text-graph-schema units integrating temporal, event, and causal structures. We benchmark our dataset on three knowledge discovery tasks, building and evaluating models for each. Results show that systems that harness causal structure are effective at identifying texts sharing similar causal meaning components rather than relying on lexical cues alone. We make our dataset and models available for research purposes.Comment: 8 pages, appendi

CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures

Abstract Learning commonsense causal and temporal relation between events is one of the major steps towards deeper language understanding. This is even more crucial for understanding stories and script learning. A prerequisite for learning scripts is a semantic framework which enables capturing rich event structures. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events. By annotating a total of 1,600 sentences in the context of 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation

Rade, Development, and the Broken Promise of Interdependence: A Buddhist Reflection on the Possibility of Post-market Economics

Bhutan's stated intention of keeping the value of happiness central to the development process is a suitable counter to the values and karma that prevail in most development strategies and ideals. Given present day realities of unprecedented, accelerating changes and paradigmatic shifts in economic, political, and social practices, any successful strategy for integration into global development processes must be creative in nature. It must, in other words, consist of an ongoing improvisation that is at once virtuosic and virtuous and that brings both greater resolution and resolve into the development process. In this essay the author wants to contribute to this effort by considering the broad landscape of development and trade concepts and practices and their implications for the trajectory of innovations needed to insure that development processes and greater economic interdependence are, indeed, liberating. The auhtor starts by reflecting on the context of present day patterns of development, raising some issues related to history and scale in assessing the effects of increasing global interdependence. He suggestes that present day patterns and scales of globalization have both generated and been generated by the extremely rapid and practically irreversible commodification of subsistence needs—a commodification that (paraphrasing Ivan Illich) has the effect of institutionalizing entirely new classes of the poor. Beyond a critical threshold and unless redirected—that is, informed by radically different values—present day patterns of interdependence will continue bringing about the conversion of communities that have been faring well into aggregates of individuals in need of welfare. Unchecked, the promise of globally extended, deep community will be broken