The Nature of Information, Semantics, and Effectiveness for Artificial Intelligence and Cognition

This manuscript puts forward claims to help address foundational gaps in understanding Cognition and Artificial General Intelligence (AGI), including the nature of Emergence, Semantics, and Information. This includes criteria for assessing true understanding in AI models. How symbolic reasoning conceptualizes phenomena is described. Without a subsymbolic perceptual level to generate concepts, there is no symbol grounding. Grounding requires dynamics outside of its own symbolization. Grounding forms the set of symbols used at the conceptual level. It is claimed that this role explains Semantics. This approach naturally leads to established research on Conceptual Spaces and has implications for Semantic Vector Spaces learned via Neural Embedding methods. It also has implications for Information Theories. A claim is made that Semantic Processes form Shannon-like microstates and macrostates, while Effective Processes constrain Semantic Processes. Unlike existing Semantic Information Theories, Semantic Processes are pre-informational. The claims provide perspective on the Mind. It is natural to conflate percepts with the modified version necessarily created when conceptualizing through explication. The ‘Hard Problem of Consciousness’ is related to this Percept/Concept distinction. Concepts are always subject to Eliminative Materialism. The nonconceptual properties of Percepts cannot be eliminated. Intrinsic are Extrinsic Emergence are distinguished. It is common to assume extrinsic emergent properties are intrinsic to the systems evoking them. This presents a challenge for proving intrinsic emergence in AI. However, criteria are proposed for claiming a theoretical system intrinsically processes information and grounds symbols. By leveraging the functional properties of Grounding, the criteria can be considered for actual systems.

Semantic process mining: A conceptual application of main tools, framework and model analysis

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

Research on the Application of Multimedia Simulation Technology

The multimedia technology has been widely applied to many engineering fields. However, because the data contained in video content is very large, it is always being a difficult problem of computer data analysis and processing to analyze the video. Based on the content analysis, this paper takes use of many technologies aimed at the problem of video, such as analysis and processing of multimedia, simulation classification of computer and computer vision and so on. At the same time, combined with the model of color information semantics and the real target tracking principle, this paper builds model and designs the algorithm for the video simulation. At last, this paper makes trajectory extraction and recognition for the real process goals of football, establishing the simulation process of football. Through the numerical simulation, it is found that frames extracted from the video capture are different from each other in the process of real football game and the recognition rate and accuracy of simulation trajectory are also not the same. Among them, when frame is 85, the effects of recognition rate and accuracy are best, which respectively reach 80% and 89%. Thus, it gains a better simulation effect.

Co-ordination and Specialisation of Semantics in a B2B Relation

To date, the B2B paradigm includes the publishing of rigid message and process standards by organisations such as GS1, BME, UBL, and UN/CEFACT. Businesses are expected to obey those standards, which may not reflect their commercial or business niche. In this chapter, a mechanism is described to simplify and formalise negotiations on bilateral information semantics and process definitions bilaterally, due to support by automated tools. The mechanism is based on ontology engineering and speech act theory. It results in XML schemas that may directly be implemented in B2B communication. Interfaces with back end systems are created on the fly. Work for this chapter was supported by the European Commission through the 7th FP project ADVANCE (http://www.advance-logistics.eu/) under grant No. 257398.