Handy broker: an intelligent product-brokering agent for m-commerce applications with user preference tracking

One of the potential applications for agent-based systems is m-commerce. A lot of research has been done on making such systems intelligent to personalize their services for users. In most systems, user-supplied keywords are generally used to help generate profiles for users. In this paper, an evolutionary ontology-based product-brokering agent has been designed for m-commerce applications. It uses an evaluation function to represent a user’s preference instead of the usual keyword-based profile. By using genetic algorithms, the agent tracks the user’s preferences for a particular product by tuning some parameters inside its evaluation function. A prototype called ‘Handy Broker’ has been implemented in Java and the results obtained from our experiments look promising for m-commerce use.     

Knowledge-based improvement of automatic image interpretation for restricted scenes: two case studies

The paper explores the usefulness and applicability of knowledge-based image interpretation. By limiting the analysis to ‘restricted’ scenes, a bottom-up strategy has been developed to improve a primal image segmentation. Two case studies are discussed: the first deals with medical X-rays, the second with satellite images (SPOT). In both projects, generic geometrical knowledge is encoded in the format of production rules. The results obtained so far are encouraging and are already of practical use. Ways to extend the knowledge bases by more specific domain knowledge are mentioned.     

Course ontology-based user’s knowledge requirement acquisition from behaviors within e-learning systems

User’s knowledge requirement acquisition and analysis are very important for a personalized or user-adaptive learning system. Two approaches to capture user’s knowledge requirement about course content within an e-learning system are proposed and implemented in this paper. The first approach is based on the historical data accumulated by an interactive question-answering process. The association space is proposed to record and formalize the historical interactive information which is used to compute user’s knowledge requirement. The second approach is based on user’s reading behavior logs in the process of reading e-documents. User’s reading actions including underline, highlight, circle, annotation and bookmark, are used to compute user’s knowledge requirement. Two experiments are conducted to implement the two proposed approaches and acquire the user’s knowledge requirement. The evaluation results show that the user models computed by two approaches are consistent and can reflect user’s real knowledge requirements accurately.     

A new fuzzy operator and its application to topology design of distributed local area networks

Topology design of distributed local area networks can be classified as a hard combinatorial optimization problem. The problem has several conflicting objectives, such as cost, reliability, network delay, and number of hops between source and destination. These objectives can conveniently be expressed in linguistic terms - a key component of fuzzy logic. This paper presents an approach based on fuzzy logic that combines the conflicting objectives into a single optimization function. A new fuzzy operator, namely, the unified AND-OR (UAO) operator is also proposed, and a decision-making approach based on fuzzy rules and preference rules is introduced. The UAO operator is empirically compared with the well-known ordered weighted averaging (OWA) operator through application to an evolutionary algorithm. Results show that the UAO operator exhibits comparatively better performance than the OWA operator.     

Supporting inconsistent rules in database systems

When a set of rules generates (conflicting) values for a virtual attribute of some tuple, the system must resolve the inconsistency and decide on a unique value that is assigned to that attribute. In most current systems, the conflict is resolved based on criteria that choose one of the rules in the conflicting set and use the value that it generated. There are several applications, however, where inconsistencies of the above form arise, whose semantics demand a different form of resolution. We propose a general framework for the study of the conflict resolution problem, and suggest a variety of resolution criteria, which collectively subsume all previously known solutions. With several new criteria being introduced, the semantics of several applications are captured more accurately than in the past. We discuss how conflict resolution criteria can be specified at the schema or the rule-module level. Finally, we suggest some implementation techniques based on rule indexing, which allow conflicts to be resolved efficiently at compile time, so that at run time only a single rule is processed.An earlier version of this work appeared under the title Conflict Resolution of Rules Assigning Values to Virtual Attributes inProceedings of the 1989 ACM-Sigmod Conference, Portland, OR, June 1989, pp. 205–214.Partially supported by the National Science Foundation under Grant IRI-9157368 (PYI Award) and by grants from DEC, HP, and AT&T.Partially supported by the National Science Foundation under Grant IRI-9057573 (PYI Award), IBM, DEC, and the University of Maryland Institute for Advanced Computer Studies (UMIACS).     

Prioritization of association rules in data mining: Multiple criteria decision approach

Data mining techniques, extracting patterns from large databases are the processes that focus on the automatic exploration and analysis of large quantities of raw data in order to discover meaningful patterns and rules. In the process of applying the methods, most of the managers who are engaging the business encounter a multitude of rules resulted from the data mining technique. In view of multi-faceted characteristics of such rules, in general, the rules are featured by multiple conflicting criteria that are directly related with the business values, such as, e.g. expected monetary value or incremental monetary value.

In the paper, we present a method for rule prioritization, taking into account the business values which are comprised of objective metric or managers’ subjective judgments. The proposed methodology is an attempt to make synergy with decision analysis techniques for solving problems in the domain of data mining. We believe that this approach would be particularly useful for the business managers who are suffering from rule quality or quantity problems, conflicts between extracted rules, and difficulties of building a consensus in case several managers are involved for the rule selection.     

An ontology-based approach to conflict resolution in Home and Building Automation Systems

Ambient intelligent systems such as Home and Building Automation Systems (HBAS) are becoming evermore accepted and capable of actuating automatically on behalf of users to fulfil their requests or enable activities. However, when multiple users interact with such systems, the requirements of activities often interfere resulting in conflicting actuations which HBAS ought to automatically detect and resolve. Yet, despite recent advances in HBAS, no ambient intelligent solution has been reported that is adequately grounded on knowledge analysis.The contributions of this article are twofold. First, it reviews relevant literature on Ambient Intelligence, conflict detection, conflict resolution and knowledge representation in HBAS. Second, it proposes and validates an ontological framework for conflict detection and resolution backed by knowledge-based analysis. Effectively, the proposed solution performs automatic environment actuations maximizing users comfort and energy efficiency.     

Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems

In the design of fuzzy-rule-based systems, we have two conflicting objectives: accuracy maximization and interpretability maximization. As a measure of interpretability, a number of criteria have been proposed in the literature. Most of those criteria have been incorporated into fitness functions in order to automatically find accurate and interpretable fuzzy systems by genetic algorithms. However, interpretability is very subjective and is rarely defined for any users beforehand. In this article, we propose the incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems. User preference is represented by a preference function which is changeable according to the user’s direct manipulation during evolution. The preference function is used as one of the objective functions in multi-objective genetic fuzzy rule selection. The effectiveness of the proposed method is examined through some case studies for the design of fuzzyrule-based classifiers.     

A dimensionality reduction approach for many-objective Markov Decision Processes: Application to a water reservoir operation problem

The operation of complex environmental systems usually accounts for multiple, conflicting objectives, whose presence imposes to explicitly consider the preference structure of the parties involved. Multi-objective Markov Decision Processes are a useful mathematical framework for the resolution of such sequential, decision-making problems. However, the computational requirements of the available optimization techniques limit their application to problems involving few objectives. In real-world applications it is therefore common practice to select few, representative objectives with respect to which the problem is solved. This paper proposes a dimensionality reduction approach, based on the Non-negative Principal Component Analysis (NPCA), to aggregate the original objectives into a reduced number of principal components, with respect to which the optimization problem is solved. The approach is evaluated on the daily operation of a multi-purpose water reservoir (Tono Dam, Japan) with 10 operating objectives, and compared against a 5-objectives formulation of the same problem. Results show that the NPCA-based approach provides a better representation of the Pareto front, especially in terms of consistency and solution diversity.     

Negotiation in a knowledge-based concurrent engineering design environment

Abstract: Computational support for concurrent engineering design presents a number of issues in the design of knowledge-based systems. A fundamental concern is the need to provide the life-cycle perspective in recommending design alternatives. It is argued that such a complex knowledge-based system should be distributed, due to the inherent problems with large knowledge bases and the maintenance of consistency. Inconsistent local views of a problem and different priorities on design issues are expected between different knowledge-based design perspectives. These inconsistencies and different priorities will lead inherently to conflict which has to be resolved. This paper addresses the issues and key variables in conflict resolution and presents a strategy that can be applied for managing the conflict resolution process. We propose negotiation as the method of analysing a conflict situation, applying an appropriate conflict resolution strategy and monitoring its performance. We present an overview of the nature of conflict and some conflict resolution strategies, and see in what situations certain strategies are appropriate.     

Personalcasting: Tailored Broadcast News

Broadcast news sources and newspapers provide society with the vast majority of real-time information. Unfortunately, cost efficiencies and real-time pressures demand that producers, editors, and writers select and organize content for stereotypical audiences. In this article we illustrate how content understanding, user modeling, and tailored presentation generation promise personalcasts on demand. Specifically, we report on the design and implementation of a personalized version of a broadcast news understanding system, MITRE’s Broadcast News Navigator (BNN), that tracks and infers user content interests and media preferences. We report on the incorporation of Local Context Analysis to both expand the user’s original query to the most related terms in the corpus, as well as to allow the user to provide interactive feedback to enhance the relevance of selected newsstories. We describe an empirical study of the search for stories on ten topics from a video corpus. By personalizing both the selection of stories and the form in which they are delivered, we provide users with tailored broadcast news. This individual news personalization provides more fine-grained content tailoring than current personalized television program level recommenders and does not rely on externally provided program metadata.     

On Resolving Conflicts Between Arguments

Argument systems are based on the idea that one can construct arguments for propositions—structured reasons justifying the belief in a proposition. Using defeasible rules, arguments need not be valid in all circumstances, therefore, it might be possible to construct an argument for a proposition as well as its negation. When arguments support conflicting propositions, one of the arguments must be defeated, which raises the question of which (sub‐) arguments can be subject to defeat. In legal argumentation, metarules determine the valid arguments by considering the last defeasible rule of each argument involved in a conflict. Since it is easier to evaluate arguments using their last rules, can a conflict be resolved by considering only the last defeasible rules of the arguments involved? We propose a new argument system where, instead of deriving a defeat relation between arguments, arguments for the defeat of defeasible rules are constructed. This system allows us to determine a set of valid (undefeated) arguments in linear time using an algorithm based on a JTMS, allows conflicts to be resolved using only the last rules of the arguments, allows us to establish a relation with Default Logic, and allows closure properties such as cumulativity to be proved. We propose an extension of the argument system based on a proposal for reasoning by cases in default logic.     

Criteria selection and analysis for single machine dynamic on-line scheduling with multiple objectives and sequence-dependent setups

Fertile opportunities exist for research involving dynamic and stochastic scheduling with multiple conflicting objectives and sequence-dependent setups as little has been reported in the literature to date. This research focuses on understanding and identifying the criteria that could be combined into a single rule using the linear weighted aggregation approach to consider the contradicting needs of cycle time and delivery accuracy. Eight dispatching criteria are compared and evaluated using discrete event simulation. In most studies, the basic concept is to combine different dispatching criterion that performs the best in each objective into a single rule but this may be insufficient. Simulation results show that it is necessary to take into account not only the criterion’s strength in optimizing a performance objective but also the degree of trade-off it has on the other conflicting performance objectives of interest. A correlation analysis of the objectives used is also presented.     

Problem structuring methods in military command and control

In an authorized military hierarchy organization, the procedure of problem solving must be co-ordinated with the tasks of planning, directing, and controlling. In most combat situations, problem solving knowledge is acquired from an expert (commander) or a single group of experts (staff) in a military organization. Therefore, these multiple actors (commander and the staff), multiple perspectives (multi-expertise and knowledge types), incommensurable and/or conflicting interests (resource allocation and distribution among staff planning), important intangibles (ambiguous quantitative or qualitative apparatus), and key uncertainties (unexpected internal and external situations) are part of unstructured problem. In this article, military strategy and tactics are acquired as case knowledge, rule knowledge, and heuristic knowledge content in terms of representing combat formations and planning mechanism to support problem structuring and the solution of military command and control. By doing so, this article presents a knowledge-based system architecture, including case base, rule base, heuristic base, and learning paradigm, for the military command and control procedure with strategic guidance (commander’s strategy and tactics) and tactical planning (staff plans generation) system functions in terms of implementing problem structuring methods in military command and control.     

Improving legal information retrieval using an ontological framework

A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods.     

PAMS: A Domain-specific Knowledge-based Parallel Machine Scheduling System

Abstract: To overcome the inefficiency of domain independent tools, a domain-specific knowledge-based shell for parallel machine scheduling problems, PAMS (PArallel Machine Scheduling System), is proposed. PAMS has the following features; mixed knowledge representation, two-stage status-driven control strategy, conflict resolution using domain theory, domain-specific syntax and primitive operators, interface with database, domain-specific input/output interface, interactive scheduling capability, built-in heuristic knowledge, multiple levels of schedule, and expansibility of the domain. A prototype of PAMS, PAMS-1, is developed using C-language on a supermicrocomputer, and the performance of PAMS-1 is evaluated. The results show that the PAMS-1 performs very well from various perspectives.     

An evidential fusion approach for activity recognition in ambient intelligence environments

With the growing emergence of ambient intelligence, ubiquitous computing, sensor networks and wireless networking technologies, “ubiquitous networked robotics” is becoming an active research domain of intelligent autonomous systems. It targets new innovative applications in which robotic systems will become part of these networks of artifacts to provide novel capabilities and various assistive services anywhere and anytime, such as healthcare and monitoring services for elderly in Ambient Assisted Living (AAL) environments. Situation recognition, in general, and activity recognition, in particular, provide an added value on the contextual information that can help the ubiquitous networked robot to autonomously provide the best service that meet the needs of the elderly. Dempster–Shafer theory of evidence and its derivatives are an efficient tool to handle uncertainty and incompleteness in smart homes and ubiquitous computing environments. However, their combination rules yield counter-intuitive results in high conflicting activities. In this paper, we propose a new approach to support conflict resolution in activity recognition in AAL environments. This approach is based on a new mapping for conflict evidential fusion to increase the efficiency and accuracy of activity recognition. It gives intuitive interpretation for combining multiple sources in all conflicting situations. The proposed approach, evaluated on a real world smart home dataset, achieves 78% of accuracy in activity recognition. The obtained results outperform those obtained with the existing combination rules.