D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification using Bayesian networks in real-time environments i.e., rapid modelling of complex situations via Bayesian networks, efficient Bayesian network inference based on incoming evidence. For rapid modelling, each network can be constructed in real-time from smaller Bayesian networks to assess a specific situation, whereas for efficient inference, a network can be broken up into sub-networks and distributed over a physical network of computers, employing parallel processing technologies (Das et al. 2002). While Bayesian networks satisfactorily handle uncertainty and casual relationships and are fairly straightforward in terms of implementation, the set of variables they support is finite and each variable has a fixed domain of possible values (Russel and Norvig, 2003). Regular Bayesian networks lack the concept of objects and relations and hence are unable to fully benefit from structure of the domain (Howard and Stumptner, 2005). In order to enable application of Bayesian networks in complex domains, relational probabilistic models attempt to rectify these limitations (Howard and Stumptner, 2005; Russell and Norvig, 2003) by including generic objects and relations. Bayesian networks also lack support for temporal reasoning and dynamic Bayesian networks have been proposed in this regard (Russell and Norvig, 2003). Also, a large amount of training data is required, as Bayesian methods are based on probability theory, to approximate the probability distributions. Sutton et al. (2004) present a Bayesian blackboard (called AIID) for information fusion that is a conventional, knowledge-based blackboard system in which knowledge sources modify Bayesian networks on the blackboard. It is proposed to carry several advantages as temporal reasoning can range from “data-driven statistical algorithms up to domain-specific, knowledge-based inference”; and “the control of intelligencegathering in the world and inference on the blackboard can be rational … grounded in probability and utility theory”. (Sutton et al. 2004) 11.3.5 Blackboard systems A blackboard system is an Artificial Intelligence application based on the blackboard architectural model. In such systems, the blackboard acts as a Common Knowledge Base (KB) which is continuously updated by several specialised Knowledge Sources (KS) to reach a viable solution to a problem. Each Knowledge Source updates the blackboard with a partial solution and iteratively, a complete solution is worked towards collaboratively by all subscribers (KSs) of the blackboard. The blackboard i.e. the common KB retains the state of the problem solution, while the KSs make changes to the blackboard. The blackboard model is suitable to tackle problems that are complex and not well-defined, and especially where the solution is a sum of its parts. The main features of blackboard architecture are multiple sources, multiple competing hypotheses, multiple levels of abstraction, feedback to the sources and the blackboard acting as a common knowledge base or an associative memory. Blackboard systems were created to deal with complex problems that are best addressed by an approach of incremental solution development. They have been used extensively in AI problems, and there exist several examples in the literature and market e.g. Hearsay I (Reddy 1973), Hearsay II (Erman, 1980), blackboards addressing signal and image understanding (Carver, 1991), planning and scheduling (Sadeh, 1998; Smith, 1985), machine translation (Nirenburg, 1989) workflows (Stegemann et al, 2007), and spatial-temporal geographic information analysis (Riadh, 2006). In essence, a blackboard system is a task independent architecture, implying that it addresses a range of problems such as design, classification, diagnosis etc. Owing to its integrated design whereby multiple knowledge sources work towards a solution incrementally, more than one technique may be employed, each as a separate knowledge source or reasoning system e.g. KS1 can be case-based while KS 2 can be heuristic i.e. rule-based (Hunt, 2002). 118
D2.1 Requirements and Specification Blackboard architectures offer several benefits such as configuration flexibility as the KSs are not rigidly connected, rather cooperate via the blackboard, hence KSs can be removed or added to the system without explicit declaration to all subscribers at any point in time. Furthermore, the user is presented with a selection of choices as the set of KSs may contain more than one source carrying out the same function to arrive at a partial solution, in which case the most efficient may be chosen. The blackboard system manages multiple levels of abstraction, using hierarchy supported by KSs, where the KSs may operate at different levels of abstraction and/or may operate between two abstraction levels for information transfer. The main disadvantages of these systems include a lack of support for task specific strategies, i.e. lack of support for domain specific problem solving strategies and they have to be implicitly added if required. Also, as the blackboard is a common knowledge base, it is directly changed by the knowledge sources and therefore consideration must be taken to oversee the responsible behaviour of knowledge sources. The scope of the system as a whole is not maintained, making it difficult to identify problems (Hunt, 2002). Salient architectures, tools and technologies Hearsay (or Hearsay I) was developed to solve complex speech recognition problems with a hierarchical abstraction space of complete and partial interpretations. It was domain restricted and not a general purpose problem solving system, however it led to an extension in the form of a new design or a sequel. Hearsay II proposed the architecture of blackboard as known today for the first time. It responded to questions and retrieved documents from a collection. It was not restricted to any specific domain. In Hearsay II, the knowledge sources work in an opportunistic fashion, i.e., each KS monitors the blackboard for an opportunity to contribute to the development of the solution. If such an opportunity is detected, the KS posts an activation entry on the agenda of the scheduler indicating its intention to contribute. The scheduler determines the suitability of any KS activation, using information in a common knowledge base and the potential impact of the knowledge source. HASP/SIAP, a signal interpretation system based on the blackboard model of Hearsay II, used AI techniques to address the surveillance problem of the activities of submarines and ships in the surveillance region. Instead of the scheduler agenda approach, HASP employed a control mechanism based on predefined events, which triggered a sequence of KSs relevant for an occurring event. Another system called TRICERO system also addressed a similar application area, employing blackboard architecture in the domain of distributed computing, to monitor a region of airspace for various aircraft activities and understand the signals to assess the situation. The problem is partitioned into independent sub-problems and solved using blackboard systems. BB1 is a task independent blackboard control architecture that offers two blackboards, namely a domain blackboard, which is essentially the blackboard as we know it, and a control blackboard, which deals with generation of a solution to the control problem. The control blackboard in BB1 works the same as standard, the only difference being that KSs are control problem solvers and the solution on the blackboard determines which domain knowledge source is to be activated next. In this way, BB1 offers increased flexibility in its control mechanism, albeit with additional computational complexity, as opposed to Hearsay II. A well-known example implementation using BB1 is a mission-planning system for an autonomous vehicle (Hayes-Roth 1985). Hearsay III provides a completely generic architecture which can be used to develop a specialised problem solver for a specific domain. The primary goal of Hearsay III is to provide representation and control facilities in order to enable the user to create a customised expert system. Van Brussel et al. (1998) used a blackboard model in a behaviour-based mobile robot system enabling task execution in unstructured real-world environments. The behaviour model consists of a number of motion 119
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CORBYS Cognitive Control Framework
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D2.1 Requirements and Specification - CORBYS

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