Towards a temporal coherence management in real-time knowledge-based systems

The multi-agent system paradigm emerges as an interesting approach in the Knowledge-Based System (KBS) field when distributed problem-solving techniques are required. On the other hand, temporal representation and reasoning problems arise in a wide range of KBS application areas where time plays a crucial role. In this paper, we show that when agents run concurrently and access common temporal data, some problems of coherence arise. We analyse the different cases in which an incoherence in temporal information can occur and provide a method to tackle this problem. In this method, conflict management is handled by means of exception handlers and control rules allowing the users to explicitly define their own strategy for temporal coherence solving.     

A blackboard model of reasoning in product liability claims evaluation

The SKADE 2 is a blackboard system that evaluates product liability claims and makes settlement decisions. The system has three knowledge sources, namely, Legal, Insurance Adjuster, and Manager. The combined expertise from each of these is required to analyze a product liability claim. A control component coordinates the communication between the various knowledge sources on the blackboard. Based on the latest changes to the data or in the hypotheses, it selects and executes the next knowledge source. The model described here reproduces the domain's decision makers' reasoning processes.The results of validation and analysis of a hypothetical case through a series of experiments with the system confirm that the blackboard is an appropriate model for development of expert systems in the product liability domain. The initial success with the SKADE 2 system suggests that further work needs to be done to see whether more complex models can be built to incorporate a broader range of determinants of product liability claims evaluation.     

A framework for knowledge-based temporal abstraction

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge.

The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed.

The knowledge-based temporal abstraction method has been implemented in the RÉSUMÉ system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.     

A freehand-sketch environment for architectural design supported by a multi-agent system

This paper presents an on-line system for capturing and interpreting architectural sketches. The prototype is based on a multi-agent system, which enables real-time management of recognition scenarios. We describe the different types of agents, their characteristics, the basic mechanisms involved in interpreting freehand architectural drawings and the collaboration modes between agents. Finally, we illustrate the general operations of the system by a short example.     

The integration of an intelligent knowledge-based system into engineering software using the blackboard structure

Over recent years there has been an increase in the use of generic Computational Fluid Dynamics (CFD) software packages spread across various application fields. This has created the need for the integration of expertise into CFD software. Expertise can be integrated into CFD software in the form of an Intelligent Knowledge-Based System (IKBS). The advantages of integrating intelligence into generic engineering software are discussed with a special view to software engineering considerations. The software modelling cycle of a typical engineering problem is identified and the respective expertise and user control needed for each modelling phase is shown. The requirements of an IKBS for CFD software are discussed and compared to current practice. The blackboard software architecture is presented. This is shown to be appropriate for the integration of an IKBS into an engineering software package. This is demonstrated through the presentation of the prototype CFD software package FLOWES.     

Improving the security level of the FUSION@ multi-agent architecture

The use of architectures based on services and multi-agent systems has become an increasingly important part of the solution set used for the development of distributed systems. Nevertheless, these models pose a variety of problems with regards to security. This article presents the Adaptive Intrusion Detection Multi-agent System (AIDeMaS), a mechanism that has been designed to detect and block malicious SOAP messages within distributed systems built by service based architectures. AIDeMaS has been implemented as part of FUSION@, a multi-agent architecture that facilitates the integration of distributed services and applications to optimize the construction of highly-dynamic multi-agent systems. One of the main features of AIDeMaS is that is employs case-based reasoning mechanisms, which provide it with great learning and adaptation capabilities that can be used for classifying SOAP messages. This research presents a case study that uses the ALZ-MAS system, a multi-agent system built around FUSION@, in order to confirm the effectiveness of AIDeMaS. The preliminary results are presented in this paper.     

The need for a new experimental environment for HCI research into multi-agent, real-time systems

Much of the current research in HCI is carried out using experimental environments based on word processors, database search, or other conventional office automation. While this approach meets many needs it lacks the power required for investigating many unconventional situations. Complex multi-agent real-time systems are not typically found in offices and cannot easily be investigated in typical word processing or office automation contexts. The paper refers to four environments where multi-agency exists in a real-time environment: flight systems, plant control, telephone networks, and complex office systems. Consideration is given to the requirements of an alternative experimental environment which could allow HCI research to explore a wider range of issues.     

Object-oriented design processes of electronic commerce in a multi-agent environment

Corporations are moving from traditional management into a world of agile and electronic business. Such businesses have continually sought to develop information technology (IT) systems, in order to assist in the decision-making of the business process. Due to rapid changes in the business environment this process is subject to ongoing examination. To ensure the agility of electronic commerce, business design process problems must be addressed by a team of specialists or intelligent agents, and be based on object-oriented design, characterized by computability, reusability, and exchangeability. This paper proposes a multi-agent system encompassing an object-oriented approach, to design processes for electronic commerce. This approach entails solutions to design processes, for decision-makers who are geographically separated and operate on differing computer platforms. By combining various objects, different types of design processes can be solved with the same agent-based framework, making this approach very responsive. The proposed methodology is applied to a real-world construct that involves combining objects to complete an electronic commerce model: in particular the logistics objective.     

The need for a new experimental environment for HCI research into multi-agent,real-time systems

Abstract

Much of the current research in HCI is carried out using experimental environments based on word processors, database search, or other conventional office automation. While this approach meets many needs it lacks the power required for investigating many unconventional situations. Complex multi-agent real-time systems are not typically found in offices and cannot easily be investigated in typical word processing or office automation contexts. The paper refers to four environments where multi-agency exists in a real-time environment: flight systems, plant control, telephone networks, and complex office systems. Consideration is given to the requirements of an alternative experimental environment which could allow HCI research to explore a wider range of issues.     

A multi-agent approach to Intelligent Transportation Systems modeling with combinatorial auctions

Challenges of urbanization require new, more flexible approaches to design of public transportation systems. Demand Responsive Transport systems (DRT) that provide a share transportation services with flexible routes and focus on optimizing of economic and environmental value are becoming an important part of public transportation. In this paper we propose a new approach to design of DRT models which considers DRT as a multi-agent system (MAS) where various autonomous agents represent interests of system’s stakeholders. The distributed nature of the MAS facilitates design of scalable implementations in modern cloud environments. We also propose a planning algorithm based on combinatorial auctions (CA) that allows to express commodity of multiple transportation scenarios by evident means of the bids. Using the mechanism of CA we may fully take into account the presence of complementariness and substitutability among the items that differ across bidders. Further, we describe design principles of our proposed software with a prototype implementation. We believe that our approach to multi-agent modeling is general enough to provide the flexibility necessary for adoption of DRT-services modeling into real-world scenarios. The results of modeling have been compared against several cases of a local bus provider and validated in a set of computational experiments.     

A multi-agent platform for content-based image retrieval

Efficient and possibly intelligent image retrieval is an important task, often required in many fields of human activity. While traditional database indexing techniques exhibit a remarkable performance in textual information retrieval current research in content-based image retrieval is focused on developing novel techniques that are biologically motivated and efficient. It is well known that humans have a remarkable ability to process visual information and to handle the volume and complexity of such information quite efficiently. In this paper, we present a content-based image retrieval platform that is based on a multi-agent architecture. Each agent is responsible for assessing the similarity of the query image to each candidate image contained in a collection based on a specific primitive feature and a corresponding similarity criterion. The outputs of various agents are integrated using one of several voting schemes supported by the system. The system’s performance has been evaluated using various collections of images, as well as images obtained in specific application domains such as medical imaging. The initial evaluation has yielded very promising results.

On point-duration networks for temporal reasoning

We present here a point-duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time. Thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations, subsuming in this way several point-based approaches to temporal reasoning. We deal with some reasoning tasks within the new models and we show that the main problem, deciding consistency, is NP-complete. However, tractable special cases are identified and we show efficient algorithms for checking consistency, finding a solution and obtaining the minimal network.     

Applications and environments for multi-agent systems

This paper addresses multi agent system (MAS) environments from an application perspective. It presents a structured view on environment-centric MAS applications. This comprises three base configurations, which MAS applications may apply directly or combine into a composite configuration. For each configuration, the paper presents key issues, requirements and opportunities (e.g. time management issues, real-world augmentation opportunities and state snapshot requirements). Thus, the paper delineates what environment technology may implement to serve MAS applications. Sample applications illustrate the configurations. Next, electronic institutions provide an example of an environment technology, addressing norms and laws in an agent society, already achieving some maturity. In comparison, application-domain specific environment technologies still are in embryonic stages.     

A blackboard approach towards integrated Farsi OCR system

An integrated OCR system for Farsi text is proposed. The system uses information from several knowledge sources (KSs) and manages them in a blackboard approach. Some KSs like classifiers are acquired a priori through an offline training process while others like statistical features are extracted online while recognizing. An arbiter controls the interactions between the solution blackboard and KSs. The system has been tested on 20 real-life scanned documents with ten popular Farsi fonts and a recognition rate of 97.05% in word level and 99.03% in character level has been achieved. An erratum to this article can be found at     

Point algebras for temporal reasoning: Algorithms and complexity

We investigate the computational complexity of temporal reasoning in different time models such as totally-ordered, partially-ordered and branching time. Our main result concerns the satisfiability problem for point algebras and point algebras extended with disjunctions—for these problems, we identify all tractable subclasses. We also provide a number of additional results; for instance, we present a new time model suitable for reasoning about systems with a bounded number of unsynchronized clocks, we investigate connections with spatial reasoning and we present improved algorithms for deciding satisfiability of the tractable point algebras.     

Real-time supply chain control via multi-agent adjustable autonomy

Real-time supply chain management in a rapidly changing environment requires reactive and dynamic collaboration among participating entities. In this work, we model supply chain as a multi-agent system where agents are subject to an adjustable autonomy. The autonomy of an agent refers to its capability to make and influence decisions within a multi-agent system. Adjustable autonomy means changing the autonomy of the agents during runtime as a response to changes in the environment. In the context of a supply chain, different entities will have different autonomy levels and objective functions as the environment changes, and the goal is to design a real-time control technique to maintain global consistency and optimality. We propose a centralized fuzzy framework for sensing and translating environmental changes to the changes in autonomy levels and objectives of the agents. In response to the changes, a coalition-formation algorithm will be executed to allow agents to negotiate and re-establish global consistency and optimality. We apply our proposed framework to two supply chain control problems with drastic changes in the environment: one in controlling a military hazardous material storage facility under peace-to-war transition, and the other in supply management during a crisis (such as bird-flu or terrorist attacks). Experimental results show that by adjusting autonomy in response to environmental changes, the behavior of the supply chain system can be controlled accordingly.     

On earliest deadline first scheduling for temporal consistency maintenance

A real-time object is one whose state may become invalid with the passage of time. A temporal validity interval is associated with the object state, and the real-time object is temporally consistent if its temporal validity interval has not expired. Clearly, the problem of maintaining temporal consistency of data is motivated by the need for a real-time system to track its environment correctly. Hence, sensor transactions must be able to execute periodically and also each instance of a transaction should perform the relevant data update before its deadline. Unfortunately, the period and deadline assignment problem for periodic sensor transactions has not received the attention that it deserves. An exception is the More-Less scheme, which uses the Deadline Monotonic (DM) algorithm for scheduling periodic sensor transactions. However, there is no work addressing this problem from the perspective of dynamic priority scheduling. In this paper, we examine the problem of temporal consistency maintenance using the Earliest Deadline First (EDF) algorithm in three steps: First, the problem is transformed to another problem with a sufficient (but not necessary) condition for feasibly assigning periods and deadlines. An optimal solution for the problem can be found in linear time, and the resulting processor utilization is characterized and compared to a traditional approach. Second, an algorithm to search for the optimal periods and deadlines is proposed. The problem can be solved for sensor transactions that require any arbitrary deadlines. However, the optimal algorithm does not scale well when the problem size increases. Hence, thirdly, we propose a heuristic search-based algorithm that is more efficient than the optimal algorithm and is capable of finding a solution if one exists.

Local strategy learning in networked multi-agent team formation

Networked multi-agent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply chain networks and sensor networks. A common challenge in many networked multi-agent systems is decentralized team formation among the spatially and logically extended agents. Even in cooperative multi-agent systems, efficient team formation is made difficult by the limited local information available to the individual agents. We present a model of distributed multi-agent team formation in networked multi-agent systems, describe a policy learning framework for joining teams based on local information, and give empirical results on improving team formation performance. In particular, we show that local policy learning from limited information leads to a significant increase in organizational team formation performance compared to a random policy.