Abstract: During the initial phase of cognitive development,
infants exhibit amazing abilities to generate novel behaviors in
unfamiliar situations, and explore actively to learn the best while
lacking extrinsic rewards from the environment. These abilities
set them apart from even the most advanced autonomous robots.
This work seeks to contribute to understand and replicate some of
these abilities. We propose the Bottom-up hiErarchical sequential
Learning algorithm with Constructivist pAradigm (BEL-CA) to
design agents capable of learning autonomously and continuously
through interactions. The algorithm implements no assumption about
the semantics of input and output data. It does not rely upon a
model of the world given a priori in the form of a set of states
and transitions as well. Besides, we propose a toolkit to analyze the
learning process at run time called GAIT (Generating and Analyzing
Interaction Traces). We use GAIT to report and explain the detailed
learning process and the structured behaviors that the agent has
learned on each decision making. We report an experiment in which
the agent learned to successfully interact with its environment and to
avoid unfavorable interactions using regularities discovered through
interaction.
Abstract: With the development of new technologies come additional opportunities for the founding of companies and new markets to be created. The barriers to entry are lowered and technology makes old business models obsolete. Incumbent companies have to be adaptable to this quickly changing environment. They have to start the process of digital maturation and they have to be able to adapt quickly to new and drastic changes that might arise. One of the biggest barriers for organizations in order to do so is their culture. This paper shows the core elements of a corporate culture that supports the process of digital maturation in incumbent organizations. Furthermore, it is explored how ideation and innovation can be used in a strategy in order to facilitate these core elements of culture that promote digital maturity. Focus areas are identified for the design of ideation strategies, with the aim to make the facilitation and incitation process more effective, short to long term. Therefore, one in-depth case study is conducted with data collection from interviews, observation, document review and surveys. The findings indicate that digital maturity is connected to cultural shift and 11 relevant elements of digital culture are identified which have to be considered. Based on these 11 core elements, five focus areas that need to be regarded in the design of a strategy that uses ideation and innovation to facilitate the cultural shift are identified. These are: Focus topics, rewards and communication, structure and frequency, regions and new online formats.
Abstract: Open Multiagent Systems (MASs) are societies in
which heterogeneous and independently designed entities (agents)
work towards similar, or different ends. Software agents are
autonomous and the diversity of interests among different members
living in the same society is a fact. In order to deal with this
autonomy, these open systems use mechanisms of social control
(norms) to ensure a desirable social order. This paper considers the
following types of norms: (i) obligation — agents must accomplish
a specific outcome; (ii) permission — agents may act in a particular
way, and (iii) prohibition — agents must not act in a specific way. All
of these characteristics mean to encourage the fulfillment of norms
through rewards and to discourage norm violation by pointing out the
punishments. Once the software agent decides that its priority is the
satisfaction of its own desires and goals, each agent must evaluate
the effects associated to the fulfillment of one or more norms before
choosing which one should be fulfilled. The same applies when agents
decide to violate a norm. This paper also introduces a framework
for the development of MASs that provide support mechanisms
to the agent’s decision-making, using norm-based reasoning. The
applicability and validation of this approach is demonstrated applying
a traffic intersection scenario.
Abstract: This study of the ‘happiness’ and ‘sustainability’ in the community of Donkeaw, Amphoe Mae Rim, Chiang Mai Province during the non-election period in Thailand, noted that their happiness levels are in the middle-average range. This was found using a mixed approach of qualitative and quantitative methods (N = 386, α = 0.05). The study explores indicators for six aspects of well-being and happiness, including, good local governance, administrative support for the health system that maintains people’s mental and physical health, environment and weather, job security and a regular income aids them in managing a sustainable lifestyle. The impact of economic security and community relationships on social and cultural capital, and the way these aspects impact on the life style of the community, affects the sustainable well-being of people. Moreover, living with transparency and participatory communication led to diverse rewards in many areas.
Abstract: In Malaysia, particularly in Sabah, the government has been promoting entrepreneurship among rural people to encourage them to earn their living by making good use of the diverse natural resources and local cultures of Sabah. Nevertheless, despite the government’s aim to encourage more local community in rural area to involve in entrepreneurship, the involvement of community in entrepreneurial activity is still low. It is crucial to identify the factors stimulate (or prevent) the involvement of rural community in Sabah in entrepreneurial activity. Therefore, this study tries to investigate the personal and contextual factors that may have impact on decision to start a business among the local community in Mantanani Island. In addition, this study also aims to identify the perceived benefits they receive from entrepreneurial activity. A structured face-to-face interview was conducted with 61 local communities in Mantanani Island. Data analysis revealed that passion, personal skills and self-confidence are the significant internal factors to entrepreneurial activity, whereas access to finance, labour and infrastructure are the significant external factors that are found to influence entrepreneurship. In terms of perceived rewards they received from taking up small business, it was found that respondents are predominantly agreed that entrepreneurship offers financial benefit than non-financial. In addition, this study also offers several suggestions for entrepreneurship development in Mantanani Island and it is hoped that this study may help the related agency to develop effective support policies in order to encourage more people in rural area to involve in entrepreneurship.
Abstract: Knowledge management (KM) literature has mainly focused on the antecedents of KM. The purpose of this study is to investigate the effect of specific human resource management (HRM) practices on employee knowledge sharing and its outcome as individual knowledge capability. Based on previous literature, a model is proposed for the study and hypotheses are formulated. The cross-sectional dataset comes from a sample of 19 knowledge intensive firms (KIFs). This study has run an item parceling technique followed by Confirmatory Factor Analysis (CFA) on the latent constructs of the research model. Employees’ collaboration and their interpersonal trust can help to improve their knowledge sharing behaviour and knowledge capability within organisations. This study suggests that in future, by using a larger sample, better statistical insight is possible. The findings of this study are beneficial for scholars, policy makers and practitioners. The empirical results of this study are entirely based on employees’ perceptions and make a significant research contribution, given there is a dearth of empirical research focusing on the subcontinent.
Abstract: In 2009, the new HRM policy was implemented in
Qatar for public sector organisations. The purpose of this research is
to examine how Qatar’s 2009 HRM policy was significant in
influencing employee retention in public organisations. The
conducted study utilised quantitative methodology to analyse the data
on employees’ perceptions of such HRM practices as Performance
Management, Rewards and Promotion, Training and Development
associated with the HRM policy in public organisations in
comparison to semi-private organisations. Employees of seven public
and semi-private organisations filled in the questionnaire based on
the 5-point Likert scale to present quantitative results. The data was
analysed with the correlation and multiple regression statistical
analyses. It was found that Performance Management had the
relationship with Employee Retention, and Rewards and Promotion
influenced Job Satisfaction in public organisations. Relationship
between Job Satisfaction and Employee Retention was also observed.
However, no significant differences were observed in the role of
HRM practices in public and semi-private organisations.
Abstract: Grid environments consist of the volatile integration
of discrete heterogeneous resources. The notion of the Grid is to
unite different users and organisations and pool their resources into
one large computing platform where they can harness, inter-operate,
collaborate and interact. If the Grid Community is to achieve this
objective, then participants (Users and Organisations) need to be
willing to donate or share their resources and permit other
participants to use their resources. Resources do not have to be
shared at all times, since it may result in users not having access to
their own resource. The idea of reward-based computing was
developed to address the sharing problem in a pragmatic manner.
Participants are offered a reward to donate their resources to the
Grid. A reward may include monetary recompense or a pro rata share
of available resources when constrained. This latter point may imply
a quality of service, which in turn may require some globally agreed
reservation mechanism. This paper presents a platform for economybased
computing using the WebCom Grid middleware. Using this
middleware, participants can configure their resources at times and
priority levels to suit their local usage policy. The WebCom system
accounts for processing done on individual participants- resources
and rewards them accordingly.
Abstract: This paper presents an economic game for sybil
detection in a distributed computing environment. Cost parameters
reflecting impacts of different sybil attacks are introduced in the sybil
detection game. The optimal strategies for this game in which both
sybil and non-sybil identities are expected to participate are devised.
A cost sharing economic mechanism called Discriminatory
Rewarding Mechanism for Sybil Detection is proposed based on this
game. A detective accepts a security deposit from each active agent,
negotiates with the agents and offers rewards to the sybils if the latter
disclose their identity. The basic objective of the detective is to
determine the optimum reward amount for each sybil which will
encourage the maximum possible number of sybils to reveal
themselves. Maintaining privacy is an important issue for the
mechanism since the participants involved in the negotiation are
generally reluctant to share their private information. The mechanism
has been applied to Tor by introducing a reputation scoring function.
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: Cognitive Dissonance can be conceived both as a concept related to the tendency to avoid internal contradictions in certain situations, and as a higher order theory about information processing in the human mind. In the last decades, this last sense has been strongly surpassed by the former, as nearly all experiment on the matter discuss cognitive dissonance as an output of motivational contradictions. In that sense, the question remains: is cognitive dissonance a process intrinsically associated with the way that the mind processes information, or is it caused by such specific contradictions? Objective: To evaluate the effects of cognitive dissonance in the absence of rewards or any mechanisms to manipulate motivation. Method: To solve this question, we introduce a new task, the hypothetical social arrays paradigm, which was applied to 50 undergraduate students. Results: Our findings support the perspective that the human mind shows a tendency to avoid internal dissonance even when there are no rewards or punishment involved. Moreover, our findings also suggest that this principle works outside the conscious level.
Abstract: This paper describes the evolution of language
politics and the part played by political leaders with reference to
the Dravidian parties in Tamil Nadu. It explores the interesting
evolution from separatism to coalition in sustaining the values of
parliamentary democracy and federalism. It seems that the
appropriation of language politics is fully ascribed to the DMK
leadership under Annadurai and Karunanidhi. For them, the Tamil
language is a self-determining power, a terrain of nationhood, and
a perennial source of social and political powers. The DMK
remains a symbol of Tamil nationalist party playing language
politics in the interest of the Tamils. Though electoral alliances
largely determine the success, the language politics still has
significant space in the politics of Tamil Nadu. Ironically, DMK
moves from the periphery to centre for getting national recognition
for the Tamils as well as for its own maximization of power. The
evolution can be seen in two major phases as: language politics for
party building; and language politics for state building with three
successive political processes, namely, language politics in the
process of separatism, representative politics and coalition. The
much pronounced Dravidian Movement is radical enough to
democratize the party ideology to survive the spirit of
parliamentary democracy. This has secured its own rewards in
terms of political power. The political power provides the means to
achieve the social and political goal of the political party.
Language politics and leadership pattern actualized this trend
though the movement is shifted from separatism to coalition.
Abstract: In a liberalized electricity market, it is not surprising
that different customers require different power quality (PQ) levels at
different price. Power quality related to several power disturbances is
described by many parameters, so how to define a comprehensive
hierarchy evaluation system of power quality (PQCHES) has become
a concerned issue. In this paper, based on four electromagnetic
compatibility (EMC) levels, the numerical range of each power
disturbance is divided into five grades (Grade I –Grade V), and the
“barrel principle" of power quality is used for the assessment of
overall PQ performance with only one grade indicator. A case study
based on actual monitored data of PQ shows that the site PQ grade
indicates the electromagnetic environment level and also expresses the
characteristics of loads served by the site.
The shortest plank principle of PQ barrel is an incentive
mechanism, which can combine with the rewards/penalty mechanism
(RPM) of consumed energy “on quality demand", to stimulate utilities
to improve the overall PQ level and also stimulate end-user more
“smart" under the infrastructure of future SmartGrid..
Abstract: Retention in the IT profession is critical for
organizations to stay competitive and operate reliably in the dynamic
business environment. Most organizations rely on compensation and
rewards as primary tools to enhance retention of employees. In this
quantitative survey-based study conducted at a large global bank, we
analyze the perceptions of 575 information technology (IT) software
professionals in India and Malaysia and find that fairness of rewards
has very little impact on retention likelihood. It is far more important
to actively involve employees in organizational activities. In
addition, our findings indicate that involvement is far more important
than information flow: the typical organizational communication to
keep employees informed.
Abstract: Green Lean Total Quality Management (LTQM) Human Resource Management (HRM) System is a system comprises of HRM in Environmental Management System (EMS) practices which is integrated to TQM with Lean Manufacturing (LM) principles. HRM is essential especially in dealing with low motivation and less productive employees. The ultimate goal of this system is to focus on achieving total human resource development that is motivated and capable to optimize their creativity to be a part of Green and Lean TQM organization. A survey questionnaire was developed and distributed to 30 highly active automotive vendors in Malaysia and analyzed by Minitab v16 and SPSS v17. It was found out companies that are practicing Green LTQM HRM practices have generated more revenue and have RND capability. However, years of company establishment do not affect the openness of the company to adapt new initiatives that can help to improve the effectiveness of the operations. It was also found out the importance of training, communication and rewards for employees. The Green LTQM HRM practices framework model established in this study hopefully will give preliminary insight especially to companies that are still looking for system that can improve their productivity from managing human resource. This is preliminary study that combined 4 awards practices, ISO/TS16949, Toyota Production System SAEJ4000, MAJAICO Lean Production System and EMS focusing on highly active companies that have been involved in MAJAICO Program and Proton Vendor Development Program. Future study can be conducted to know the status at other industry as well as case study pertaining to this system.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: In this paper, we propose a single sample path based
algorithm with state aggregation to optimize the average rewards of
singularly perturbed Markov reward processes (SPMRPs) with a
large scale state spaces. It is assumed that such a reward process
depend on a set of parameters. Differing from the other kinds of
Markov chain, SPMRPs have their own hierarchical structure. Based
on this special structure, our algorithm can alleviate the load in the
optimization for performance. Moreover, our method can be applied
on line because of its evolution with the sample path simulated.
Compared with the original algorithm applied on these problems of
general MRPs, a new gradient formula for average reward
performance metric in SPMRPs is brought in, which will be proved
in Appendix, and then based on these gradients, the schedule of the
iteration algorithm is presented, which is based on a single sample
path, and eventually a special case in which parameters only
dominate the disturbance matrices will be analyzed, and a precise
comparison with be displayed between our algorithm with the old
ones which is aim to solve these problems in general Markov reward
processes. When applied in SPMRPs, our method will approach a fast
pace in these cases. Furthermore, to illustrate the practical value of
SPMRPs, a simple example in multiple programming in computer
systems will be listed and simulated. Corresponding to some practical
model, physical meanings of SPMRPs in networks of queues will be
clarified.