China 
Africa 
Explore chapters and articles related to this topic
Knowledge in AI
Published in Janet Finlay, Alan Dix, An Introduction to Artificial Intelligence, 2020
Facts and rules such as these can be represented easily in PROLOG. However, predicate logic and PROLOG have a limitation, which is that they operate under what is known as the closed world assumption. This means that we assume that all knowledge in the world is represented: the knowledge base is complete. Therefore any fact that is missing is assumed to be false. PROLOG uses a problem-solving strategy called negation as failure, which means that it returns a result of false if it is unable to prove a goal to be true. This relies on the closed world assumption (Reiter 1978). Such an assumption is useful when all relevant facts are represented but can cause problems when the knowledge base is incomplete.
Introduction to Expert Systems
Published in Chris Nikolopoulos, Expert Systems, 1997
Prolog employs the closed world assumption (CWA) hypothesis in order to deal with negation, (see Chapter 2, Section 10). Everything that cannot be deduced from the knowledge base is assumed to be false (negation by failure). The built in predicate not takes one argument, which is a conjunction of literals and returns true if the search for a proof for the argument fails, false otherwise.
Forecasting with jury-based probabilistic argumentation
Published in Journal of Applied Non-Classical Logics, 2023
Francesca Toni, Antonio Rago, Kristijonas Čyras
Arguments J, A and P can be obtained from the following rules in the instance of ABA corresponding to logic programming (see Bondarenko et al., 1997), where assumptions are negation-as-failure literals, of the form , with literals l being their contrary: Specifically, argument J is obtained from rules , argument A is obtained from the (probabilistic) assumption redundancies, and argument P is obtained from rule . These arguments are supported by negation as failure assumptions and as well as assumptions that correspond to (binary) random variables and in a simple Bayesian network consisting simply of two nodes labelled by these variables.
Automatic planning in cognitive training: application to multiple sclerosis
Published in Human–Computer Interaction, 2023
Mauro Gaspari, Federica Pinardi, Dario Signorello, Franca Stablum, Sara Zuppiroli
Names starting with a question mark represent variables. This action can be executed if all the predicates in the precondition match with facts in the current state, here negation as failure is used. The positive facts in the effect part are added to the state, and the negative ones removed. The following predicates are used in this rule: (at ?thing ?place): an object ?thing is at position ?place.(visitor ?v): ?v is a visitor of the ZooSafari.(road ?from ?to): a road that connect two places.(to-discover ?place): a place that need still to be visited.(able-to-walk ?from ?to): it is possible to walk from two places.
Reasoning on objects and grasping using description logics
Published in Advanced Robotics, 2019
Nicola Vitucci, Giuseppina Gini
The choices of the reasoner, the logics, and the storage system influence the type of queries that could be executed on a knowledge base: TBox queries depend on the reasoner, while ABox queries depends on the query engine implementation. SPARQL-DL [28] provides a unified framework for both kinds of queries, but it is not a standard yet. Query languages such as SPARQL1.1, after the reasoning phase (which has to be performed by an external reasoner), can be used to match currently known data and even to ‘close the world’, for example by implementing negation by failure.