Aditya, Somak (Arizona State University) | Yang, Yezhou (University of Maryland, College Park) | Baral, Chitta (Arizona State University) | Fermuller, Cornelia (Associate Research Scientist, University of Maryland, College Park) | Aloimonos, Yiannis (University of Maryland, College Park)
In this paper we explore the use of visual common-sense knowledge and other kinds of knowledge (such as domain knowledge, background knowledge, linguistic knowledge) for scene understanding. In particular, we combine visual processing with techniques from natural language understanding (especially semantic parsing), common-sense reasoning and knowledge representation and reasoning to improve visual perception to reason about finer aspects of activities.
Multi-context systems (MCSs) define a versatile framework for integrating and reasoning about knowledge from different (heterogeneous) sources. In MCSs, different types of non-monotonic reasoning are characterized by different semantics such as equilibrium semantics and grounded equilibrium semantics [Brewka and Eiter, 2007]. We introduce a novel semantics of MCSs, a supported equilibrium semantics. Our semantics is based on a new notion of support. The strength'' of supports determines a spectrum of semantics that, in particular, contains the equilibrium and grounded equilibrium semantics. In this way, our supported equilibrium semantics generalizes these previously defined semantics. Moreover, the strength'' of supports gives us a measure to compare different semantics of MCSs.
Conditionals are generally considered the backbone of human (and AI) reasoning: the "if-then" connection between two propositions is the stepping stone of arguments and a lot of the research effort in formal logic has focused on this kind of connection. A conditional connection satisfies different properties according to the kind of arguments it is used for. The classical material implication is appropriate for modelling the "ifthen" connection as it is used in Mathematics, but the equivalence between the material implication A B and A B is not appropriate for many other contexts.
Probabilistic context free grammars (PCFG) have been the core of the probabilistic reasoning based parsers for several years especially in the context of the NLP. Multi entity bayesian networks (MEBN) a First Order Logic probabilistic reasoning methodology and is widely adopted and used method for uncertainty reasoning. Further upper ontology like Probabilistic Ontology Web Language (PR-OWL) built using MEBN takes care of probabilistic ontologies which model and capture the uncertainties inherent in the domain's semantic information. The paper attempts to establish a link between probabilistic reasoning in PCFG and MEBN by proposing a formal description of PCFG driven by MEBN leading to usage of PR-OWL modeled ontologies in PCFG parsers.
Artificial intelligence (AI) is a widely used term that conjures notions of fantasy, the future, or even threat. This is not surprising considering the multitude of movies which dramatise the role of artificial intelligence and what it may become. In reality, artificial intelligence is a branch of computer science which aims to "understand and build intelligent entities by automating human intellectual tasks". These processes have contributed to numerous technological advances across various industries, for example. It is now quite common to see articles about the latest AI development -- check out these robots which flip burgers!