Ordering based decision making: a survey

Decision making is the crucial step in many real applications such as organization management, financial planning, products evaluation and recommendation. Rational decision making is to select an alternative from a set of different ones which has the best utility (i.e., maximally satisfies given criteria, objectives, or preferences). In many cases, decision making is to order alternatives and select one or a few among the top of the ranking. Orderings provide a natural and effective way for representing indeterminate situations which are pervasive in commonsense reasoning. Ordering based decision making is then to find the suitable method for evaluating candidates or ranking alternatives based on provided ordinal information and criteria, and this in many cases is to rank alternatives based on qualitative ordering information. In this paper, we discuss the importance and research aspects of ordering based decision making, and review the existing ordering based decision making theories and methods along with some future research directions

Temporal reasoning for intuitive specification of context-awareness

One of the most important challenges of the creation of intelligent environments is the specifications of what intelligent behaviours the system will exhibit. The processing of these situations can be computationally demanding. We report on the advances of the specification of a rule-based language which allows for the natural expression of situations of interest as those which occur on Intelligent Environments. The language focuses on quasi real-time situations and includes new temporal operators which allow a natural reference to time instants and to intervals. We explained how the system is implemented and how the system was validated within a Smart Office scenario

Early adductive reasoning for blind signal separation

We demonstrate that explicit and systematic incorporation of abductive reasoning capabilities into algorithms for blind signal separation can yield significant performance improvements. Our formulated mechanisms apply to the output data of signal processing modules in order to conjecture the structure of time-frequency interactions between the signal components that are to be separated. The conjectured interactions are used to drive subsequent signal separation processes that are as a result less blind to the interacting signal components and, therefore, more effective. We refer to this type of process as early abductive reasoning (EAR); the “early” refers to the fact that in contrast to classical Artificial Intelligence paradigms, the reasoning process here is utilized before the signal processing transformations are completed. We have used our EAR approach to formulate a practical algorithm that is more effective in realistically noisy conditions than reference algorithms that are representative of the current state of the art in two-speaker pitch tracking. Our algorithm uses the Blackboard architecture from Artificial Intelligence to control EAR and advanced signal processing modules. The algorithm has been implemented in MATLAB and successfully tested on a database of 570 mixture signals representing simultaneous speakers in a variety of real-world, noisy environments. With 0 dB Target-to-Masking Ratio (TMR) and no noise, the Gross Error Rate (GER) for our algorithm is 5% in comparison to the best GER performance of 11% among the reference algorithms. In diffuse noisy environments (such as street or restaurant environments), we find that our algorithm on the average outperforms the best reference algorithm by 9.4%. With directional noise, our algorithm also outperforms the best reference algorithm by 29%. The extracted pitch tracks from our algorithm were also used to carry out comb filtering for separating the harmonics of the two speakers from each other and from the other sound sources in the environment. The separated signals were evaluated subjectively by a set of 20 listeners to be of reasonable quality

Concept of a Robust & Training-free Probabilistic System for Real-time Intention Analysis in Teams

Die Arbeit beschäftigt sich mit der Analyse von Teamintentionen in Smart Environments (SE). Die fundamentale Aussage der Arbeit ist, dass die Entwicklung und Integration expliziter Modelle von Nutzeraufgaben einen wichtigen Beitrag zur Entwicklung mobiler und ubiquitärer Softwaresysteme liefern können. Die Arbeit sammelt Beschreibungen von menschlichem Verhalten sowohl in Gruppensituationen als auch Problemlösungssituationen. Sie untersucht, wie SE-Projekte die Aktivitäten eines Nutzers modellieren, und liefert ein Teamintentionsmodell zur Ableitung und Auswahl geplanten Teamaktivitäten mittels der Beobachtung mehrerer Nutzer durch verrauschte und heterogene Sensoren. Dazu wird ein auf hierarchischen dynamischen Bayes’schen Netzen basierender Ansatz gewählt

Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research

New Perspectives on Games and Interaction

This volume is a collection of papers presented at the 2007 colloquium on new perspectives on games and interaction at the Royal Dutch Academy of Sciences in Amsterdam. The purpose of the colloquium was to clarify the uses of the concepts of game theory, and to identify promising new directions. This important collection testifies to the growing importance of game theory as a tool to capture the concepts of strategy, interaction, argumentation, communication, cooperation and competition. Also, it provides evidence for the richness of game theory and for its impressive and growing application

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Models and evaluation of human-machine systems

"September 1993.""Prepared for: International Atomic Energy Association [sic], Wagramerstrasse 5, P. 0. Box 100 A-1400 Vienna, Austria."Part of appendix A and bibliography missingIncludes bibliographical referencesThe field of human-machine systems and human-machine interfaces is very multidisciplinary. We have to navigate between the knowledge waves brought by several areas of the human learning: cognitive psychology, artificial intelligence, philosophy, linguistics, ergonomy, control systems engineering, neurophysiology, sociology, computer sciences, among others. At the present moment, all these disciplines seek to be close each other to generate synergy. It is necessary to homogenize the different nomenclatures and to make that each one can benefit from the results and advances found in the other. Accidents like TMI, Chernobyl, Challenger, Bhopal, and others demonstrated that the human beings shall deal with complex systems that are created by the technological evolution more carefully. The great American writer Allan Bloom died recently wrote in his book 'The Closing of the American Mind' (1987) about the universities curriculum that are commonly separated in tight departments. This was a necessity of the industrial revolution that put emphasis in practical courses in order to graduate specialists in many fields. However, due the great complexity of our technological world, we feel the necessity to integrate again those disciplines that one day were separated to make possible their fast development. This Report is a modest trial to do this integration in a holistic way, trying to capture the best tendencies in those areas of the human learning mentioned in the first lines above. I expect that it can be useful to those professionals who, like me, would desire to build better human-machine systems in order to avoid those accidents also mentioned above

What Socrates Began: An Examination of Intellect Vol. 1

Walter E. Russell Endowed Chair in Philosophy and Education Symposium 1988https://digitalcommons.usm.maine.edu/facbooks/1237/thumbnail.jp

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents--or short passages--in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020