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Domain Knowledge Representations
Published in Nawari O. Nawari, Building Information Modeling, 2018
Most of the original formulations of AI systems are based on Minsky’s work and represent knowledge as a mixture of facts and rules. The facts were pairs of data items and their values. The rules operated on facts and allowed the system to infer new facts from the given sets of data. The current approach works very similarly, and if one changes anything, such as a fact taking on a new value, then everything in the system could potentially change. Conventional AI systems use the Rete algorithm like a net to reveal from the given data any rules that rely on the changed data item. These rules are then fired, and any data items that are changed are added back to the data set. Again, the net is used to search for rules using these new data items, and the process is continued until no data items change.
A new distributed parallel reasoning using MapReduce
Published in Amir Hussain, Mirjana Ivanovic, Electronics, Communications and Networks IV, 2015
The Rete algorithm (Doorenbos 1995) is a pattern matching-algorithm for implementing production rule systems. It uses a dataflow network to represent the production conditions. The network has two parts: the alpha part and the beta part. The alpha part filters data on working memory elements (WME) to find the collection in accordance with each individual condition of rules. The beta part combines different WMEs, typically WME lists from a beta memory with individual WMEs from an alpha memory. (Walzer et al. 2008) The workflow of Rete algorithm as shown in Figure 3.
Function block-enabled operation planning and machine control in Cloud-DPP
Published in International Journal of Production Research, 2023
Mohammad Givehchi, Yongkui Liu, Xi Vincent Wang, Lihui Wang
Case study 1 focuses on knowledge-based operation planning for MF-FBs. In this case study, the ability of the system to generate operation plans is tested against the following two scenarios: (1) a job is assigned to identical machines with different cutting tool sets, and (2) some cutting tools can be removed from the sets to test the adaptability of the system to new situations. In the subsequent case study, the unit has been enriched further to support more types of features and conditions. As mentioned before, MF-FBs are independent in the way they plan and perform the related material removal and they might have heterogeneous implementations with different techniques. However, all the MF-FBs already designed and implemented for the prototype system are based on the internal knowledge-based operation planning method explained in the methodology. Drools, an open source Java-based production rules system, is used in the development of the system. The object-oriented rule-based inference engine with a Rete algorithm implementation provided by Drools is used for operation planning. Moreover, its development environment and tools for rule authoring were used in development of the system.
Relationship between UAVs and Ambient objects with threat situational awareness through grid map-based ontology reasoning
Published in International Journal of Computers and Applications, 2022
Myung-Joong Jeon, Hyun-Kyu Park, Batselem Jagvaral, Hyung-Sik Yoon, Yun-Geun Kim, Young-Tack Park
Jess is a rule engine and scripting environment written using Java language. It can build the Java software, which has the capacity to reason using knowledge in the form of declarative rules. Jess is intuitive and light, and it is useful for building applications because it can be used with full java APIs. Jess uses the Rete algorithm to process rules. Rete is optimized to solve many-to-many matching problems effectively.