Using 3D sound as a navigational aid in virtual environments

As current virtual environments are less visually rich than real-world environments, careful consideration must be given to their design to ameliorate the lack of visual cues. One important design criterion in this respect is to make certain that adequate navigational cues are incorporated into complex virtual worlds. In this paper we show that adding 3D spatialized sound to a virtual environment can help people navigate through it. We conducted an experiment to determine if the incorporation of 3D sound (a) helps people find specific locations in the environment, and (b) influences the extent to which people acquire spatial knowledge about their environment. Our results show that the addition of 3D sound did reduce time taken to locate objects in a complex environment. However, the addition of sound did not increase the amount of spatial knowledge users were able to acquire. In fact, the addition of 3D auditory sound cues appears to suppress the development of overall spatial knowledge of the virtual environment.     

Limited Rationality in Action: Decision Support for Military Situation Assessment

Information is a force multiplier. Knowledge of the enemy's capability and intentions may be of far more value to a military force than additional troops or firepower. Situation assessment is the ongoing process of inferring relevant information about the forces of concern in a military situation. Relevant information can include force types, firepower, location, and past, present and future course of action. Situation assessment involves the incorporation of uncertain evidence from diverse sources. These include photographs, radar scans, and other forms of image intelligence, or IMINT; electronics intelligence, or ELINT, derived from characteristics (e.g., wavelength) of emissions generated by enemy equipment; communications intelligence, or COMINT, derived from the characteristics of messages sent by the enemy; and reports from human informants (HUMINT). These sources must be combined to form a model of the situation. The sheer volume of data, the ubiquity of uncertainty, the number and complexity of hypotheses to consider, the high-stakes environment, the compressed time frame, and deception and damage from hostile forces, combine to present a staggeringly complex problem. Even if one could formulate a decision problem in reasonable time, explicit determination of an optimal decision policy exceeds any reasonable computational resources. While it is tempting to drop any attempt at rational analysis and rely purely on simple heuristics, we argue that this can lead to catastrophic outcomes. We present an architecture for a ``complex decision machine' that performs rational deliberation to make decisions in real time. We argue that resource limits require such an architecture to be grounded in simple heuristic reactive processes. We thus argue that both simple heuristics and complex decision machines are required for effective decision making in real time for complex problems. We describe an implementation of our architecture applied to the problem of military situation assessment.     

More than bin packing: Dynamic resource allocation strategies in cloud data centers

Resource allocation strategies in virtualized data centers have received considerable attention recently as they can have substantial impact on the energy efficiency of a data center. This led to new decision and control strategies with significant managerial impact for IT service providers. We focus on dynamic environments where virtual machines need to be allocated and deallocated to servers over time. Simple bin packing heuristics have been analyzed and used to place virtual machines upon arrival. However, these placement heuristics can lead to suboptimal server utilization, because they cannot consider virtual machines, which arrive in the future. We ran extensive lab experiments and simulations with different controllers and different workloads to understand which control strategies achieve high levels of energy efficiency in different workload environments. We found that combinations of placement controllers and periodic reallocations achieve the highest energy efficiency subject to predefined service levels. While the type of placement heuristic had little impact on the average server demand, the type of virtual machine resource demand estimator used for the placement decisions had a significant impact on the overall energy efficiency.     

EXPERIENCE-BASED KNOWLEDGE REPRESENTATION: SOEKS

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process.

For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment.     

Discovering salient prosodic cues and their interactions for automatic story segmentation in Mandarin broadcast news

This paper investigates speech prosody for automatic story segmentation in Mandarin broadcast news. Prosodic cues effectively used in English story segmentation deserve a re-investigation since the lexical tones of Mandarin may complicate the expressions of pitch declination and reset. Our data-oriented study shows that story boundaries cannot be clearly discriminated from utterance boundaries by speaker normalized pitch features due to their large variations across different Mandarin syllable tones. We thus propose to use speaker- and tone-normalized pitch features that can provide clear separations between utterance and story boundaries. Our study also shows that speaker-normalized pause duration is quite effective to separate between story and utterance boundaries, while speaker-normalized speech energy and syllable duration are not effective. Experiments using decision trees for story boundary detection reinforce the difference between English and Chinese, i.e., speaker- and tone-normalized pitch features should be favorably adopted in Mandarin story segmentation. We show that the combination of different prosodic cues can achieve a very high F-measure of 93.04% due to the complementarity between pause, pitch and energy. Analysis of the decision tree uncovered five major heuristics that show how speakers jointly utilize pause duration and pitch to separate speech into stories.     

INVEX: Investment Advisory Expert System

Abstract: Capital investment is a very important business decision, because it is largely irreversible and usually long-term. We believe that the use of expert systems as a decision aid for interactive investment decision-making offers several advantages to an unassisted human decision-maker, or even to the human/conventional-DSS combination. For example, the expert system could capture valuable information about so called 'hard data', and about the attitude towards some drastic changes in the environment, that are not easy to include in any quantitative method. However, if one expert can play a very important role in the decision-making process, perhaps different experts can be even more valuable, bringing different approaches and somewhat different sets of information to a decision-making situation. Our multiparadigm blackboard framework, called BEST (Blackboard-based Expert Systems Toolkit), allows its user to combine knowledge coming from different experts, and to use different methods/paradigms to capture that knowledge, according to the type of partial problem at hand where each knowledge source is a single paradigm program. In investment decision-making, judgmental investment ranking and selection from expert economists, embedded in a rule-based knowledge source, might be combined with decisions from operational research methods (embedded in a knowledge source that fully respects multicriteria optimization paradigm) and from risk analysis method (embedded in a conventional, procedural knowledge source). When the decisions being combined come from different types of knowledge sources, redundancy is likely to be reduced and the combined decision is likely to be more objective.     

Cooperative decision making in a knowledge grid environment

A knowledge grid is an intelligent interconnection environment, built on top of a computational grid, to facilitate the creation of virtual organizations. An important feature of a virtual environment is its support for collaborative decision-making. A major difficulty with current approaches is that they cannot easily handle environments where decision makers are added or removed dynamically. In this article, a new approach to alter the number of decision makers dynamically is suggested. The amount of decision accuracy made by each decision maker, for a given subject, is determined subjectively considering the other decision makers’ opinions. The effect of decisions made by each decision maker varies gradually considering its past decisions. Assuming each decision maker provides a fuzzy answer set in response to each decision problem, an operator for fusing of the decision makers’ decision sets is suggested. The aim of the fusion is improvement of the decision quality. The fusing operator provides a fuzzy answer set that is a function of the accuracy possibility of each decision maker and its fuzzy answer set.     

How do Simple Rules `Fit to Reality' in a Complex World?

The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science.     

Using 3D sound as a navigational aid in virtual environments

As current virtual environments are less visually rich than real-world environments, careful consideration must be given to their design to ameliorate the lack of visual cues. One important design criterion in this respect is to make certain that adequate navigational cues are incorporated into complex virtual worlds. In this paper we show that adding 3D spatialized sound to a virtual environment can help people navigate through it. We conducted an experiment to determine if the incorporation of 3D sound (a) helps people find specific locations in the environment, and (b) influences the extent to which people acquire spatial knowledge about their environment. Our results show that the addition of 3D sound did reduce time taken to locate objects in a complex environment. However, the addition of sound did not increase the amount of spatial knowledge users were able to acquire. In fact, the addition of 3D auditory sound cues appears to suppress the development of overall spatial knowledge of the virtual environment.     

Understanding Perceived Trust to Reduce Regret

Trust is fundamental for promoting the use of online services, such as e‐commerce or e‐health. Understanding how users perceive trust online is a precondition to create trustworthy marketplaces. In this article, we present a domain‐independent general trust perception model that helps us to understand how users make online trust decisions and how we can help them in making the right decisions, which minimize future regret. We also present the results of a user study describing the weight that different factors in the model (e.g., security, look&feel, and privacy) have on perceived trust. The study identifies the existence of a positive correlation between the user's knowledge and the importance placed on factors such as security and privacy. This indicates that the impact factors as security and privacy have on perceived trust is higher in users with higher knowledge.     

An adaptive Student Centered Curriculum for an intelligent training system

An intelligent tutoring system customizes its presentation of knowledge to the individual needs of each student based on a model of the student. Student models are more complex than other user models because the student is likely to have misconceptions. We have addressed several difficult issues in reasoning about a student's knowledge and skills within a real-time simulation-based training system. Our conceptual framework enables important aspects of the tutor's reasoning to be based upon simple, comprehensible representations that are the basis for a Student Centered Curriculum. We have built a system for teaching cardiac resuscitation techniques in which the decisions abouthow to teach are separated from the decisions aboutwhat to teach. The training context (i.e., choice of topics) is changed based on a tight interaction between student modeling techniques and simulation management. Although complex student models are still required to support detailed reasoning about how to teach, we argue that the decision about what to teach can be adequately supported by qualitatively simpler techniques, such as overlay models. This system was evaluated in formative studies involving medical school faculty and students. Construction of the student model involves monitoring student actions during a simulation and evaluating these actions in comparison with an expert model encoded as a multi-agent plan. The plan recognition techniques used in this system are novel and allow the expert knowledge to be expressed in a form that is natural for domain experts.     

Flexible Multi-Agent Decision Making Under Time Pressure

Autonomous agents need considerable computational resources to perform rational decision making. These demands are even more severe when other agents are present in the environment. In these settings, the quality of an agent's alternative behaviors depends not only on the state of the environment, but also on the actions of other agents, which in turn depend on the others' beliefs about the world, their preferences, and further on the other agents' beliefs about others, and so on. The complexity becomes prohibitive when large number of agents are present and when decisions have to be made under time pressure. In this paper, we investigate strategies intended to tame the computational burden by using offline computation in conjunction with online reasoning. We investigate two approaches. First, we use rules compiled offline to constrain alternative actions considered during online reasoning. This method minimizes overhead, but is not sensitive to changes in real-time demands of the situation at hand. Second, we use performance profiles computed offline and the notion of urgency (i.e., the value of time) computed online to choose the amount of information to be included during online deliberation. This method can adjust to various levels of real-time demands, but incurs some overhead associated with iterative deepening. We test our framework with experiments in a simulated anti-air defense domain. The experiments show that both procedures are effective in reducing computation time while offering good performance under time pressure.     

AN EXTENDED HIERARCHICAL LINGUISTIC MODEL FOR DECISION‐MAKING PROBLEMS

In multi‐expert decision making (MEDM) problems the experts provide their preferences about the alternatives according to their knowledge. Because they can have different knowledge, educational backgrounds, or experiences, it seems logical that they might use different evaluation scales to express their opinions. In the present article, we focus on decision problems defined in uncertain contexts where such uncertainty is modeled by means of linguistic information, therefore the decision makers would use different linguistic scales to express their evaluations on the alternatives, i.e., multigranular linguistic scales. Several computational approaches have been presented to manage multigranular linguistic scales in decision problems. Although they provide good results in some cases, still present limitations. A new approach, so‐called extended linguistic hierarchies, is presented here for managing multigranular linguistic scales to overcome those limitations, an MEDM case study is given to illustrate the proposed method.     

"Tsunagari-kan" Communication: Design of a New Telecommunication Environment and a Field Test with Family Members Living Apart

In this article, a new telecommunication environment for tsunagari-kan communication that enables users to exchange situational cues continuously and interactively via a network is described. In face-to-face communication or when communicators share one space physically, people exchange many situational cues. These cues allow one to sense or "feel" the other person, and they evoke emotional responses such as smiles and feelings of happiness. This tsunagari-kan communication aims at evoking thoughts about others in users' minds from cues or signs and at engendering tsunagari-kan (a sense of closeness to others), even when the individuals are in separate locations. This new style of telecommunication offers people comfort as if they are together, and it eventually helps them keep and foster their personal relationships. As the first step in verifying this concept, a communication terminal for family members living apart was developed, and a field test was run. This terminal facilitates the exchange of data about people's unconscious presence/motion cues and conscious touch signs. With an analysis of the field test results, the validity of this concept was confirmed.     

A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

The assembly flowshop scheduling problem has been addressed recently in the literature. There are many problems that can be modeled as assembly flowshop scheduling problems including queries scheduling on distributed database systems and computer manufacturing. The problem has been addressed with respect to either makespan or total completion time criterion in the literature. In this paper, we address the problem with respect to a due date-based performance measure, i.e., maximum lateness. We formulate the problem and obtain a dominance relation. Moreover, we propose three heuristics for the problem: particle swarm optimization (PSO), Tabu search, and EDD. PSO has been used in the areas of function optimization, artificial neural network training, and fuzzy system control in the literature. In this paper, we show how it can be used for scheduling problems. We have conducted extensive computational experiments to compare the three heuristics along with a random solution. The computational analysis indicates that Tabu outperforms the others for the case when the due dates range is relatively wide. It also indicates that the PSO significantly outperforms the others for difficult problems, i.e., tight due dates. Moreover, for difficult problems, the developed dominance relation helps reduce error by 65%.     

Genetics-based learning of new heuristics: rational scheduling ofexperiments and generalization

We present new methods for the automated learning of heuristics in knowledge lean applications and for finding heuristics that can be generalized to unlearned domains. These applications lack domain knowledge for credit assignment; hence, operators for composing new heuristics are generally model free, domain independent, and syntactic in nature. The operators we have used are genetics based; examples of which include mutation and cross over. Learning is based on a generate and test paradigm that maintains a pool of competing heuristics, tests them to a limited extent, creates new ones from those that perform well in the past, and prunes poor ones from the pool. We have studied three important issues in learning better heuristics: anomalies in performance evaluation; rational scheduling of limited computational resources in testing candidate heuristics in single objective as well as multiobjective learning; and finding heuristics that can be generalized to unlearned domains. We show experimental results in learning better heuristics for: process placement for distributed memory multicomputers, node decomposition in a branch and bound search, generation of test patterns in VLSI circuit testing, and VLSI cell placement and routing     

Have a look around: the effect of physical environments on risk behaviour in work-related versus non-work related decision-making tasks

Abstract

Due to ubiquitous computing, knowledge workers do not only work in typical work-associated environments (e.g. the office) but also wherever it best suits their schedule or preferences (e.g. the park). In two experiments using laboratory and field methods, we compared decision making in work and non-work environments. We hypothesised that participants make riskier work-related decisions when in work-associated environments and riskier non-work-related decisions in non-work-associated environments. Therefore, if environment (work vs. non-work) and decision-making task (work-related vs. non-work-related) are incongruent, then risk-taking should be lower, as the decision maker might feel the situation is unusual or inappropriate. Although results do not reveal that work-associated environments generally encourage riskier work-related decisions (and likewise for non-work), we found environmental effects on decision making when including mood as a moderator.

Practitioner summary: Mobile workers are required to make decisions in various environments. We assumed that decisions are more risky when they are made in a fitting environment (e.g. work-related decisions in work environments). Results of the two experiments (laboratory and field) only show an environmental effect when mood is included as a moderator.     

Should I Share That? Prompting Social Norms That Influence Privacy Behaviors on a Social Networking Site

This study examines how explicit and implicit cues to social norms affect disclosure and privacy decisions in a Social Network Site (SNS) context. Study 1 revealed that participants' disclosure behavior adhered to explicit cues indicating disclosure frequency norms, while implicit social norm cues (i.e., surveillance primes) acted to increase overall disclosure frequency and affect disclosure accuracy when explicit cues discourage disclosure. Study 2 explored how these cues affected privacy‐setting decisions and found that explicit cues indicating others' privacy settings could increase how strictly participants set their privacy settings, but the implicit cues had no effect. These results suggest that explicit cues about SNS norms can trigger bandwagon heuristic processing, and that, under limited circumstances, surveillance primes can affect self‐disclosure.     

SelectCare—in aid of psychotherapists' treatment decisions

In this paper we describe SelectCare, a computer system to support psychotherapists in their decision making for the treatment of depressed patients. Treatment decision tasks are complex and ill-structured: it is not clear what the relevant bits of information are and how these should be integrated into a correct decision, or even what a correct decision is. Still, treatment decisions are important and merit thorough consideration. We therefore set ourselves the goal of equipping psychotherapists with a system that helps them improve the completeness and overview of their considerations for treatment decisions.     

A multi-level scheduler for batch jobs on grids

This paper proposes a two-level scheduler for dynamically scheduling a continuous stream of sequential and multi-threaded batch jobs on grids, made up of interconnected clusters of heterogeneous single-processor and/or symmetric multiprocessor machines. The scheduler aims to schedule arriving jobs respecting their computational and deadline requirements, and optimizing the hardware and software resource usage. At the top of the hierarchy a lightweight meta-scheduler (MS) classifies incoming jobs according to their requirements, and schedules them among the underlying resources balancing the workload. At cluster level a Flexible Backfilling algorithm carries out the job machine associations by exploiting dynamic information about the environment. Scheduling decisions at both levels are based on job priorities computed by using different sets of heuristics. The different proposals have been compared through simulations. Performance figures show the feasibility of our approach.