Knowledge Base Of Agent Research Articles

Математические и графовые модели специализированных мультиагентных систем с распределенным решением прикладных задач

The concept of a specialized multi-agent system (SMAS) as a system designed for a distributed solution of either one applied problem or a highly limited range of applied problems is considered. Such problems frequently require a large number of homogeneous operations, which can lead to a significant increase in the size of the system and, as a result, to inefficient control and a drop in performance. Therefore, it is advisable to divide a large-sized open SMAS into classes of agents and find relationships between them. This corresponds to dividing the applied task T into subtasks and determining relations between these subtasks. Such a path entails a two-level distributed control over SMAS agents. At the first and second levels, respectively, the relations of agents within classes and between classes of agents are studied. This paper proposes a multi-model approach to constructing, modeling, and analyzing SMAS. The properties of the agent and its mathematical model, described by a 5-digit set, as well as the mathematical model of the class of agents, described by a 12-digit set, which includes such components as the collective knowledge base of agents, the collective communication environment, mental, social, and other components, are discussed. Relations between classes of agents are reflected in a mathematical model of open SMAS. If a partition of a problem T is represented by an undirected acyclic graph GR′ whose terminal vertices have degree one and whose interior vertices have degree two, then GR′ is defined as a simple tree. In the case when any class of agents is a complete graph К then the Cartesian product CP′ = GR′ × Kn, according to the proved theorem, is a graph model of an open SMAS, consisting of the components К and connectives between them. According to the corollary of this theorem, it is possible to pass from CP′ to the multigraph model MYL′ = (MV′, ME′) of the system, if the components К are associated with the vertices of this model mv′ com  MV′, and the connectives are associated with its edges me′ con1  ME′. Moreover, the number of edges between any two vertices MYL′ is equal to n. The analysis of multigraph models of open SMAS of large sizes will significantly reduce the complexity of their study at the structural level.

Aligning and linking entity mentions in image, text, and knowledge base

A picture is worth a thousand words, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed reveal different and complementary information that, if combined will result in more information than the sum of that contained in a single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with the agent’s background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this article, after providing a precise definition of the VTKEL task, we present two datasets called VTKEL1k* and VTKEL30k. These datasets consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The datasets can be used for training and evaluating algorithms of the VTKEL task. Successively, we introduce a baseline algorithm called VT-LinKEr (Visual-Textual-Knowledge Entity Linker) for the solution of the VTKEL task. We evaluate the performances of VT-LinKEr on both datasets. We then contribute a supervised algorithm called ViTKan (Visual-Textual-Knowledge Alignment Network). We trained the ViTKan algorithm using features data of the VTKEL1k* dataset. The experimental results on VTKEL1k* and VTKEL30k datasets show that ViTKan substantially outperforms the baseline algorithm.

Model of an autonomous agent forming its own simple knowledge base

In the present work, a computer model of an autonomous agent that forms its own knowledge base is constructed and studied. We consider a rather simple knowledge base of an agent, which forms and uses a cognitive map of the external world. This map is the agent knowledge base. The external world of the agent is a two-dimensional cellular environment. In this environment, the agent can eat and move. The agent prefers certain cells for eating. Namely, such cells form the nutrient spots of the world. The model was investigated by means of computer simulation. We analyze the processes of formation of a cognitive map in the agent knowledge base that characterizes the nutrient spots of the world.

Performance of Health Insurance Sector During COVID-19 Pandemic: An Overview of Perception Among Insured Individuals in West Bengal

The health insurance sector is playing a satisfying role in tackling the Covid-19 related health exigencies in West Bengal, at present. This Sector has shown a sustainable performance through properly maintaining of policy costs, launching of new policies, undertaking of proper publicity, making of health policy clauses dynamic and through extensive knowledge base of insurance agents regarding policy features. The insured persons mainly encounter problems during claim settlements, in this pandemic period.

If Nothing Is Accepted -- Repairing Argumentation Frameworks

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.

Model-based development of a multi-agent system for controlling material flow systems

Abstract The rising number of product variants requires flexible manufacturing systems, including their internal material flow systems (MFSs). An approach to design MFSs reconfigurably is the use of a decentralized control based on software agents. For implementing an agent-based control approach for MFSs this paper presents a meta model describing the knowledge base of individual agents and the overall control task to be fulfilled by the MFS.

Ontology-based GFML agent for patent technology requirement evaluation and recommendation

Patent technology requirement evaluation and recommendation are critical for patent strategy, patent management, and patent usage in an organization. This paper proposes a patent technology evaluation and recommendation agent based on a soft-computing approach to enhance patent expansibility and technology transfer. First, we investigate the relationship between patent technology and patent owners, such as academic institutes or organizations, and integrate the collected patent data with the characteristics of organizations to establish a popular patent ontology for general academic institutes or organizations. Then, the patent’s suitability for a specific organization is determined based on concepts extracted using Chinese Knowledge Information Processing. Next, we refer to the Japan Patent Office evaluation index and intellectual property quotient to describe the knowledge base and rule base of the patent quality evaluation agent by using fuzzy markup language (FML). A comprehensive patent quality evaluation mechanism is implemented, and the genetic algorithm is adopted to improve the performance of the proposed agent. Additionally, the patent requirement level evaluation mechanism infers the patent requirement level according to the basic information of an organization. Finally, we present a novel FML-based patent requirement recommendation agent to recommend a patent for an organization by considering the suitability of the patent technology for such an organization, the results of the comprehensive patent quality evaluation process, and the results of the evaluation of the demander’s patent requirements. According to the results, the proposed agent is feasible for patent recommendation. In the future, we will combine an intelligent robot with the GFML agent to assist humans or organizations in recommending an appropriate patent.

A hybrid and learning agent architecture for network intrusion detection

A hybrid learning software agent for network intrusion detection is proposed.The agent combines a reactive and a case-based reasoning performance components.A learning component allows to improve the performance by learning reactive rules.The performance and effectiveness of the agent were evaluated.Evaluation results show that the agent is well-suited for dynamic environments. Learning is an effective way for automating the adaptation of systems to their environment. This ability is especially relevant in dynamic environments as computer networks where new intrusions are constantly emerging, most of them having similarities and occurring frequently. Traditional intrusion detection systems still have limitations of adaptability because they are just able to detect intrusions previously set in system design. This paper proposes HyLAA a software agent architecture that combines case-based reasoning, reactive behavior and learning. Through its learning mechanism, HyLAA can adapt itself to its environment and identify new intrusions not previously specified in system design. This is done by learning new reactive rules by observing recurrent good solutions to the same perception from the case-based reasoning system, which will be stored in the agent knowledge base. The effectiveness of HyLAA to detect intrusions using case-based reasoning behavior, the accuracy of the classifier learned by the learning component and both the performance and effectiveness of HyLAA to detect intrusions using hybrid behavior with learning and without learning were evaluated, respectively, by conducting four experiments. In the first experiment, HyLAA exhibited good effectiveness to detect intrusions. In the second experiment the classifiers learned by the learning component presented high accuracy. Both the hybrid agent behavior with learning and without learning (third and fourth experiment, respectively) presented greater effectiveness and a balance between performance and effectiveness, but only the hybrid behavior showed better effectiveness and performance as long as the agent learns.

Detection of emergency situations during the work of batch vacuum apparatus

Modern requirements for control of sugar technological complexes make the task of development and implementation of innovative control systems.Solution of new challenges is fully possible only when using intelligent control systems. The promising way is the development and implementation of control systems based on the principles of situational control. Use of such systems allows creating intelligent multi-agent system, based on a knowledge base of agent environment.It was designed and built two-level frame structure of detection of conflict situations for the batch vacuum apparatus control. The resulting two-level frame control structure defines emergency and conflict situations in the format of environment categories: actions – A, situations – S. Such structure of frame building allows create multi-level control systems and enables rapid change control system through the mechanism of scenarios.Algorithms of frame of conflict situations created with the help of language of situational control and situational description of the object.Application of the proposed frame structure in the case control systems (agent systems) allow more efficient, at a qualitative level, to monitor the mass crystallization of sugar, integrate equipment in the technological complexes, as well as in the multi-level system of production control.A promising area of application of multi-level systems is their use in decision-making systems, organizational and technical systems.

Agent-Based Reasoning in Medical Planning and Diagnosis Combining Multiple Strategies

Medical reasoning describes a form of qualitative inquiry that examines the cognitive (thought) processes involved in making medical decision. In this field the goal for diagnostic reasoning is assessing causes of observed conditions in order to make informed choices about treatment. In order to design a diagnostic reasoning method we merge ideas from a hypothetic-deductive method and the Domino model. In this setting, we introduce the so called Hypothetic-Deductive-Domino (HD-D) algorithm. In addition, a multi-agent approach is presented, which takes advantage of the HD-D algorithm for illuminating different standpoints in a diagnostic reasoning and assessment process, and for reaching a well-founded conclusion. This multi-agent approach is based on the so called Observer and Validating agents. The Observer agents are supported by a deductive inference process and the Validating agents are supported by an abductive inference process. The knowledge bases of these agents are captured by a class of possibilistic logic programs. Hence, these agents are able to deal with qualitative information. The approach is illustrated by a real scenario from diagnosing dementia diseases.

Diagnostics of Distributed Intelligent Control Systems: Reasoning using Ontologies and Hidden Markov Models

Abstract The distributed intelligent control systems based on multi-agent systems paradigm bring many important features, including their flexibility and extensibility. These features are even more apparent when the agents use ontologies as a base for their knowledge management – such ontologies can be regarded as models that to some degree drive the operation of the system. However, much of the information from knowledge bases of agents can be used also for other important ability of a control system – the diagnostics. In this paper, we demonstrate and discuss two approaches to diagnostics – one based on description logic reasoning and the other one based on Hidden Markov Models. Both of these approaches are illustrated on sample scenario from a transportation system.

A Semantical Account of Progression in the Presence of Uncertainty

Building on a general theory of action by Reiter and his colleagues, Bacchus et al. give an account for formalizing degrees of belief and noisy actions in the situation calculus. Unfortunately, there is no clear solution to the projection problem for the formalism. And, while the model has epistemic features, it is not obvious what the agent's knowledge base should look like. Also, reasoning about uncertainty essentially resorts to second-order logic. In recent work, Gabaldon and Lakemeyer remedy these shortcomings somewhat, but here too the utility seems to be restricted to queries (with action operators) about the initial theory. In this paper, we propose a fresh amalgamation of a modal fragment of the situation calculus and uncertainty, where the idea will be to update the initial knowledge base, containing both ordinary and (certain kinds of) probabilistic beliefs, when noisy actions are performed. We show that the new semantics has the right properties, and study a special case where updating probabilistic beliefs is computable. Our ideas are closely related to the Lin and Reiter notion of progression.

Design and Evolution of an Agent Based System Applied to an Autocatalytic Network

In this paper, the design and implementation of an agent-based framework to safeguard an auto-catalytic reactor network against external disturbances is discussed. The autocatalytic reactor network producing a robust, host species is attacked by an unmeasured, un-sustained disturbance of a non-robust invading species. The invading species adversely affects host species and manifests different behavioural responses depending on its magnitude and location of injection. The agent framework is employed to detect and counteract the invasion. The agent architecture consists of the physical layer and multiple invasion detection and local operator agents. Based on the operation state of the process, the detection agent raise a variety of flags (for example, “normal operation”, “severe invasion” etc.), whereas the operator agents take control actions based on the flags raised by the detection agent. The agent knowledge in the form of rule-based heuristics designed based on process knowledge gained through simulation under open-loop. The agent knowledge base consists of several parameters which affect their behaviours. Here, we propose a methodology to optimize the parameters critical to agent design. With this method, the agent system evolves in its structure, which facilitates more accurate responses.

Pre-argumentative reasoning

Intelligent agents configure a new generation of virtual entities that perform various autonomous tasks on behalf of others, namely the humans. On the other hand, the information society requires the development of new and more intelligent methods, tools and theories to analyse, define, model and specify agent-based systems. It is under this presupposition that in this work are introduced a pre-argumentative reasoning scheme, which need to be able to take into account, in a negotiation, factors such as temporality, priority, delegation, gratitude and agreement, enabling the agents to react and pro-act accordingly. This argument-based negotiation among agents has much to gain from the use of Extended Logic Programming and Incomplete Information, in terms of the argument's evaluation. Indeed, it is based on this substratum that are presented the bases for a pre-contract negotiation via argumentation, where pre-contract negotiation will be defined as a protocol which, once enforced, will allow the exchange of messages containing proposals, counter-proposals, critiques, justifications or even explanations. The terms of a negotiation are also set, adjusting the contract according to the agent's knowledge base. In fact, an important contribution of this work relies on the presentation of the basic definitions and the general model of pre-contract-based negotiations via argumentation.

A smart advisory service for e-commerce applications based upon intelligent agents

Due to the ongoing progress within the development of B2B and B2C concepts a focus upon the aquisition and serving of individual customer’s needs can be identified. The best possible, automated service is the major goal. The corresponding systems need to be equipped with sufficient information about the customer himself as well as about his immediate environment. Appropriate means therefore are semantic networks. Deployed as a modelling formalism within the knowledge base of Intelligent Agents they ensure a flexible and also descriptive representation of the necessary knowledge about products, services and the customers themselves.

The Fabricare scheduling prototype suite: Agent interaction and knowledge base

This work presents a prototype system (named Fabricare) for the scheduling of manufacturing orders, based on holonic manufacturing systems using extended logic programming. The system is composed of several holons that cooperate among themselves using an extension to the contract net protocol named contract net with constraint propagation protocol. This protocol enables explicit cooperation among service providers that use constraint propagation in order to guarantee the precedence relationships between the different operations participating in the same task. Some precautions have been taken to avoid conflicts and combinatorial explosion. The complexity analysis of the protocol has been performed. The main agents in this system are task and resource agents; the knowledge bases of these agents are presented. Also, a notation for representation of incomplete information on an agent's knowledge base is presented.

Toward recognizing and conveying an attitude of doubt via natural language

This paper addresses the dual problems of recognizing when an agent is expressing an attitude of doubt toward a proposition and of realizing natural language utterances that effectively convey such doubt. It presents naturally occurring examples that illustrate the wide variety of ways in which doubt can be expressed. The paper presents a recognition algorithm that uses linguistic, contextual, and world knowledge to determine when an agent is expressing doubt at a proposition and to identify the proposition that is being doubted. It then presents a realization algorithm that uses these knowledge sources to formulate utterances that will convey doubt when an agent cannot accept a proposition due to conflict between it and some other proposition in the agent's knowledge base.

An agent architecture for strategy-centric adaptive QoS control in flexible videoconference system

In this paper, we propose an agent architecture to improve flexibility of a videoconference system with strategy-centric adaptive QoS (Quality of Service) control mechanism. The proposed architecture realizes more flexibility by changing their QoS control strategies dynamically. To switch the strategies, system considers the properties of problems occurred on QoS and status of problem solving process. This architecture is introduced as a part of knowledge base of agent that deals with cooperation between software module of videoconference systems. We have implemented the mechanism, and our prototype system shows its capability of flexible problem solving against the QoS degradation, along with other possible problems within the given time limitation. Thus we confirmed that the proposed architecture can improve its flexibility of a videoconference system compared to traditional systems.

Verification of multiple agent knowledge-based systems

This paper's purpose is extending single agent verification tests to systems with multiple agent knowledge bases. Particularly, this paper identifies potentially anomalous situations occurring between agent knowledge bases. For example, consider one agent with rule if A then B and another agent with rule if A then C, so agents have a consequence conflict for the same condition. In this setting, agents are constantly at odds, unable to reconcile differences. Alternatively, the following rules are found in one agent (if A then B, if C then A), while another agent has rule (if B then C). With two interacting rule bases, a dialogue starting with A cycles indefinitely, A → B → C → A, depending on relationships between these two agents. Additionally, with traditional problems, this paper also identifies new issues, like agent isolation property. One potential approach to identifying anomalies in multiple agent systems is comparing each subset of agents' knowledge base determining existence verification issues. Where agents' number is small this approach is feasible. However, for even medium size systems this approach explodes computationally. As a result, alternative approaches are needed. This paper finds many multiple agent verification tests are conducted on metarule set generated from all rules contained in each of agents' knowledge bases minimizing computational effort and minimizing need for new verification concepts development. A wide range of anomalies are identified for multiple agent systems. Anomalies are system properties not necessarily incorrect. An important part of system structure is anomalous knowledge. For example, multiple agent systems employ negotiations and other devices facilitating agent interaction, mitigating anomalies. However, anomalies signal a system error. Ultimately, determination to dispostion of the anomaly is likely the responsibility of the developer.

Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain

This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.