Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving

Pt. I. Fundamentals of hybrid intelligent systems and agents -- 1. Introduction -- 2. Basics of hybrid intelligent systems -- 3. Basics of agents and multi-agent systems -- Pt. II. Methodology and framework -- 4. Agent-oriented methodologies -- 5. Agent-based framework for hybrid intelligent systems --6. Matchmaking in middle agents -- Pt. III. Application systems -- 7. Agent-based hybrid intelligent system for financial investment
planning -- 8. Agent-based hybrid intelligent system for data mining -- Pt. IV. Concluding remarks -- 9. The less the more -- App. Sample source codes of the agent-based financial planning system

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data—a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented

Developing and testing a model of knowledge utilisation through marketing research

Little research has examined the return on marketing research, be that financial or knowledge acquisition. Furthermore, there has been insufficient research into the factors affecting the conduct of marketing research. This paper investigates and reports on a conceptual model proposed by Yaman (2000), which explores knowledge acquisition, dissemination, and utilisation through marketing research. The study specifically explores and attempts to replicate the model’s conceptual structure. The data were collected electronically via emails and an HTML web-form questionnaire, with a sample of 182 being obtained. Using structural equation modelling, the results obtained indicated an adequate fit for a modified Yaman model to the data from this particular sample.

Do knowledge and experience have specific roles in triage decision-making?

Accuracy of triage decisions is a major influence on patient outcomes. Triage nurses' knowledge and experience have been cited as influential factors in triage decision-making. The aim of this article is to examine the independent roles of factual knowledge and experience in triage decisions. All of the articles cited in this review were research papers that examined the relationship between triage decisions and knowledge and/or experience of triage nurses. Numerous studies have shown that factual knowledge is an important factor in improving triage decisions. Although a number of studies have examined the role of experience as an independent influence on triage decisions, none have found a significant relationship between experience and triage decision-making. Factual knowledge appears to be more important than years of emergency nursing or triage experience in triage decision accuracy. Many triage education programs are underpinned by the assumption that knowledge acquisition will result in improved triage decisions. A better understanding of the relationships between clinical decisions, knowledge, and experience is pivotal for the rigorous evaluation of education programs.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents' track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified.

School and local environmental knowledge, what are the links? A case study among indigenous adolescents in Oaxaca, Mexico.

Understanding environmental learning is the first step to constructing successful environmental education programs. Little research has addressed the relation between the environmental knowledge learned inside and outside schools. Environmental educators and ethnobiologists have worked independently, without assessing how school and local environmental knowledge relate to each other. This research examines school and local environmental knowledge acquisition of 95 Mexican indigenous adolescents. Multivariate regression analysis was used to assess (1) school and local environmental knowledge overlap and (2) the association between individual environmental knowledge and socio-demographic characteristics. Data show that school and local environmental knowledge are not associated in a statistically significant way. A possible explanation for the finding is that the two forms of knowledge are complementary because they exist in parallel. Adolescents’ school and local environmental knowledge is associated with their level of schooling, but not with parental occupation in community forestry. The use of traditional pedagogical practices at school and the loss of traditional culture at home might hamper indigenous adolescents’ environmental learning.

Co-ordination in multi-agent systems

A multi-agent system is a complex software system which is composed of many relative autonomous smaller softwares called agents. The research on multi-agent systems is concerned with the interaction and coordination among these agents to let them help each other to solve complicated problems, such as finance investment management. The principal contributions represented by these 50 selected papers are "cooperation under uncertainty in distributed expert systems (DESs)", "a tool and algorithms to build DESs", and "information gathering and decision making in multi-agent systems (MASs)".

Learning other agents' preferences in multiagent negotiation

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

Negotiating agents that learn about others’ preferences

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents’ preferences via past interactions. Over time, the agents can incrementally update their models of other agents’ preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situation where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

An incremental meta-cognitive-based scaffolding fuzzy neural network

The idea of meta-cognitive learning has enriched the landscape of evolving systems, because it emulates three fundamental aspects of human learning: what-to-learn; how-to-learn; and when-to-learn. However, existing meta-cognitive algorithms still exclude Scaffolding theory, which can realize a plug-and-play classifier. Consequently, these algorithms require laborious pre- and/or post-training processes to be carried out in addition to the main training process. This paper introduces a novel meta-cognitive algorithm termed GENERIC-Classifier (gClass), where the how-to-learn part constitutes a synergy of Scaffolding Theory - a tutoring theory that fosters the ability to sort out complex learning tasks, and Schema Theory - a learning theory of knowledge acquisition by humans. The what-to-learn aspect adopts an online active learning concept by virtue of an extended conflict and ignorance method, making gClass an incremental semi-supervised classifier, whereas the when-to-learn component makes use of the standard sample reserved strategy. A generalized version of the Takagi-Sugeno Kang (TSK) fuzzy system is devised to serve as the cognitive constituent. That is, the rule premise is underpinned by multivariate Gaussian functions, while the rule consequent employs a subset of the non-linear Chebyshev polynomial. Thorough empirical studies, confirmed by their corresponding statistical tests, have numerically validated the efficacy of gClass, which delivers better classification rates than state-of-the-art classifiers while having less complexity.

Planning for country: empowering built environment students with Indigenous protocols and knowledge

Increasingly, Built Environment (BE) professionals, including planner, architect and landscape architect practitioners, are becoming involved in the planning and design of projects for, and in direct consultation with Indigenous communities and their proponents. These projects range from inserting Indigenous cultural landscape analysis into planning schemes, including Indigenous protocols and aspirations in policy statements; designing cultural centres, information centres and housing; drafting cultural tourism strategies and devising cross-cultural land management plans. This entails working with Indigenous communities or their nominated representatives as stakeholders in community engagement, consultation, and planning processes. Critically, BE professionals must be able to plan and design with regard to Indigenous community’s cultural protocols, issues and values. Yet many (domestic and or international) students graduate with little or no comprehension of Indigenous knowledge systems or the protocols for engagement with the communities in which they are required to work, whether they be Australian or international Indigenous communities. Contextually, both PIA and the planning academe have struggled with coming to terms with this realm over the last 10 years. This paper will report on a recently completed Australian Government Office of Learning & Teaching (OLT) funded research project that has sought to improve opportunities to improve the knowledge and skills of tertiary students in the BE professions through the enhancement of their competency, appreciation and respect for Indigenous protocols and processes that also implicates the professional accreditation systems that these courses are accountable. It has proposed strategies and processes to expose students in the BE professions to Australian Indigenous knowledge and cultural systems and the protocols for engaging with Indigenous Australians about their rights, interests, needs and aspirations. Included in these findings is the provision of a tool that enables and offers guidance to BE tertiary students and academics how to enhance comprehension, exposure to, and knowledge and cultural systems of, Indigenous Australians. While the scope of this report is cross-BE, this paper will focus upon the planning practice, policy and academe realms.

Marketing research and its value: the experience of Australian organisations

There is a continuing need for organisations to identify the returns obtained from marketing research, such as direct knowledge acquisition or the indirect results of decisions made using this information (e.g., financial returns). This paper reports on a study based on a conceptual model proposed by earlier researchers that explored knowledge acquisition derived from marketing research, together with its dissemination and utilisation. An adequate fit for the model was found using primary data from a sample of decision-makers in Australian organisation. The findings of this empirical study show an association between marketing research, knowledge utilisation, and the performance of the organisations sampled.

The effects of specific educational preparation on emergency nurses` clinical decisions regarding supplemental oxygen administration

Abstract The use of supplemental oxygen by emergency nurses has important implications for patient outcomes, yet there is significant variability in oxygen administration practises. Specific education related to oxygen administration increases factual knowledge in this domain; however, the impact of knowledge acquisition on nurses' clinical decisions is poorly understood. This study aimed to examine the effect of educational preparation on 20 emergency nurses' decisions regarding the assessment of oxygenation and the use of supplemental oxygen. A pre-test/post-test, quasi-experimental design was used. The intervention was a written, self-directed learning package. The major effects of the completion of the learning package included no change in the number or types of parameters used by nurses to assess oxygenation, a significant decrease in the selection of simple masks, a significant increase in the selection of air entrainment masks, fewer hypothetical outcomes of unresolved respiratory distress and more hypothetical outcomes of decreased respiratory distress. As many nursing education programs are aimed at increasing factual knowledge, while experience remains relatively constant, a greater understanding of the relationship between factual knowledge and clinical decisions is needed if educational interventions are to improve patient outcomes.

An agent-based hybrid intelligent system for financial investment planning

Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. This paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. This system currently consists of 13 different agents. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.