{'fraction_non_alphanumeric': 0.018377693282636248, 'fraction_numerical': 0.0025348542458808617, 'mean_word_length': 5.551867219917012, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Semantic reliability in distributed AI systems

Generally the concept of reliability has been interpreted as applied to hardware and software and has been based upon the composition of components assuming perfect semantic transactions between them, i.e., with no consideration to variation in the interpretation of the meanings of messages between various components. In multi-agent systems, where the agents interact with each other more than merely sending and receiving messages, cooperative decisions are made based upon beliefs, desires, intentions and autonomy of individual agents. In such cases even if the components as well as the interconnections are error free in the classical sense, there can be serious failures due to semantic variability and consequently the concepts of reliability need to be extended to semantics as well. This paper explores this new concept of semantic reliability and its relationship to the system reliability and information extraction processes. This paper examines the communication between agents and semantic error modes in multi-agent systems. Rao and Georgeff's (1995) BDI (Belief-Desire-Intention) model of intelligent agents is used to decompose the semantic variation into its contributing parts from various subsystems comprising the agents. From this the impact and the risk management strategies including fault tolerance are evolved. World representation, domain ontologies, and knowledge representation are brought out as important determinants of error control. Fault tolerance design based on goal hierarchy is suggested.