Algorithms for Hidden Markov Models With Imprecisely Specified Parameters

Hidden Markov models (HMMs) are widely used models for sequential data. As with other probabilistic graphical models, they require the specification of precise probability values, which can be too restrictive for some domains, especially when data are scarce or costly to acquire. We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions. Our models have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. Efficient inference algorithms are developed to address standard HMM usage such as the computation of likelihoods and most probable explanations. Experiments with real data show that the use of imprecise probabilities leads to more reliable inferences without compromising efficiency.

A Characteristic Function Approach to Inconsistency Measures for Knowledge Bases.

Knowledge is an important component in many intelligent systems.
Since items of knowledge in a knowledge base can be conflicting, especially if
there are multiple sources contributing to the knowledge in this base, significant
research efforts have been made on developing inconsistency measures for
knowledge bases and on developing merging approaches. Most of these efforts
start with flat knowledge bases. However, in many real-world applications, items
of knowledge are not perceived with equal importance, rather, weights (which
can be used to indicate the importance or priority) are associated with items of
knowledge. Therefore, measuring the inconsistency of a knowledge base with
weighted formulae as well as their merging is an important but difficult task. In
this paper, we derive a numerical characteristic function from each knowledge
base with weighted formulae, based on the Dempster-Shafer theory of evidence.
Using these functions, we are able to measure the inconsistency of the knowledge
base in a convenient and rational way, and are able to merge multiple knowledge
bases with weighted formulae, even if knowledge in these bases may be
inconsistent. Furthermore, by examining whether multiple knowledge bases are
dependent or independent, they can be combined in different ways using their
characteristic functions, which cannot be handled (or at least have never been
considered) in classic knowledge based merging approaches in the literature.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Context-dependent Combination of Sensor Information in Dempster-Shafer Theory for BDI

There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.

Kinect and Episodic Reasoning for Human Action Recognition

This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.

Laughter Research: A Review of the ILHAIRE Project

Laughter is everywhere. So much so that we often do not even notice it. First, laughter has a strong connection with humour. Most of us seek out laughter and people who make us laugh, and it is what we do when we gather together as groups relaxing and having a good time. But laughter also plays an important role in making sure we interact with each other smoothly. It provides social bonding signals that allow our conversations to flow seamlessly between topics; to help us repair conversations that are breaking down; and to end our conversations on a positive note.

Development of a novel immersive interactive virtual reality cricket simulator for cricket batting

Research in the field of sports performance is constantly developing new technology to help extract meaningful data to aid in understanding in a multitude of areas such as improving technical or motor performance. Video playback has previously been extensively used for exploring anticipatory behaviour. However, when using such systems, perception is not active. This loses key information that only emerges from the dynamics of the action unfolding over time and the active perception of the observer. Virtual reality (VR) may be used to overcome such issues. This paper presents the architecture and initial implementation of a novel VR cricket simulator, utilising state of the art motion capture technology (21 Vicon cameras capturing kinematic profile of elite bowlers) and emerging VR technology (Intersense IS-900 tracking combined with Qualisys Motion capture cameras with visual display via Sony Head Mounted Display HMZ-T1), applied in a cricket scenario to examine varying components of decision and action for cricket batters. This provided an experience with a high level of presence allowing for a real-time egocentric view-point to be presented to participants. Cyclical user-testing was carried out, utilisng both qualitative and quantitative approaches, with users reporting a positive experience in use of the system.

Special issue on the 6th Portuguese Conference on Automatic Control (Controlo 2004)

The Portuguese Association of Automatic Control (APCA) organizes, every two years, the Portuguese Conference on Automatic Control. Its 6th edition (Controlo 2004) was held from 7 to 9 June, 2004 at the University of Algarve, Faro, Portugal, by its Centre for Intelligent Systems (CSI). CONTROLO 2004 International Program Committee (IPC) has decided, from the very start, to ask for submission of full draft papers, to encourage special sessions with well-defined themes, and for student papers. All papers have been reviewed by three separate reviewers. From the 122 contributions submitted, the IPC selected 89 oral papers, 20 special session papers, and 5 student posters. CONTROLO 2004 Technical Programme consists of 33 oral sessions (5 being special sessions) and 1 poster session, covering a broad range of control topics, both from theory and applications. The programme also includes three plenary lectures, given by leading experts in the field, Professors Ricardo Sanz, João Miranda Lemos and Rolf Isermann.

Computational intelligence

The Centre for Intelligent Systems (CIS) is a multidisciplinary research and development centre, founded in 2001, in a very young university, the University of Algarve, in the south of Portugal. The centr's mission is to promote fundamental research in Computational Intelligence (CI) methodology.

A quarter century of creative chaos: Xinhua News Agency 1980-2005

Chinese media in the context of China's rise have puzzled many scholars who used to understand media and communications phenomena by employing the theories generated from a few affluent Western democracies, notably the US. As a result, a complex but more accurate picture has been ignored. Under numerous theoretical polarizations, the contemporary social world seems little changed but polarized. This thesis aims to propose a different approach endeavoring to 'de-Westernize' or 'internationalize' media and communications studies. As a starting point, this study focuses on the globalization debate, Chinese media and news agency studies. The thesis has investigated the Chinese news agency, Xinhua, by employing Fuzzy Logic which captures the complexity of the change in the agency's business structure and journalistic practices over last 25 years. The change is also examined by scrutinizing the role of journalists in the interrelations of Xinhua with its news sources, media and nonmedia clients, and other news agencies. A combination of archive study and 94 semistructured interviews conducted in Beijing, Shanghai, Guangzhou, Hong Kong, Macau and London provides an inclusive account of the Chinese news institution. The key research findings drawn from the empirical research into Xinhua have justified the central argument of this thesis: Crisp Logic or the 'either/or' approach has failed to explain the dynamics of the change to the media system based in a 'non-Western' society. The numerous theoretical polarizations generated by Crisp Logic to a large extent have distorted the understanding of the contemporary social world by polarizing it. Fuzzy Logic serves better(though it is not the only choice)than the traditional approach to reflect on the set of variables existing between the two poles created by Crisp Logic. This thesis is the first doctorate research in the UK and other English-speaking countries to investigate Xinhua by 'going inside' the news institution's headquarters, local branches and overseas bureaus. This is the first comprehensive academic study of the agency, which not only examines the agency's recent change in business structure and journalistic practices, but also provides a historical account of the agency and its relationship with other social institutions. This is the first media study that employs Fuzzy Logic to understand the globalization theory, Chinese media and news agencies.

Agent-Based Simulation of the Evolution of Enterprise Carbon Assets

Carbon assets have the value of carbon emission reduction in enterprises and are closely relevant to business images and competitiveness. In this paper, the connotation of carbon assets is clarified. The definition of carbon assets in enterprise business contexts are also provided. In addition, an interactive evolution framework is established to demonstrate the emergent property of carbon assets using multi-agent-based simulation, which can bring a new perspective for enterprises to manage their carbon assets and improve low-carbon competitiveness.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.