Michael Kandefer

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This paper presents a soft information fusion framework for creating a propositional graph from natural language messages with an emphasis on producing these graphs for fusion with other messages. The framework utilizes artificial intelligence techniques from natural language understanding, knowledge representation, and information retrieval.
The Trial The Trail is an interactive drama running on an immersive VR system. Imagine Tarkovsky’s Stalker, crossed with Alice Through the Looking Glass, crossed with Monty Python and the Holy Grail. Now imagine embarking on a guided journey through this warped yet familiar landscape. Your guides are two intelligent agents, Patofil and Filopat. We consider(More)
We are demonstrating several intelligent agents built according to the MGLAIR (Modal Grounded Layered Architecture with Integrated Reasoning) agent architecture. The top layer of MGLAIR is implemented in SNePS and its acting subsystem, SNeRE (the SNePS Rational Engine). The major demonstration will be act 3 of The Trial The Trail, an interactive drama(More)
We demonstrate the use of SNeRE, the acting component of the SNePS knowledge representation, reasoning, and acting system, by showing its use to implement a wumpus world agent [Russell and Norvig, 1995]1. For this purpose, we use SNePS 2.6.2, which consists of SNePS 2.6.1 [Shapiro et al., 2004] plus some patch files. We usually name our SNePSbased agents(More)
We present a solution to McCarthy’s Second Telephone Number Problem. This problem requires an agent to: realize that it lacks some knowledge to complete a task; know the external knowledge sources it can use to obtain the knowledge; know how to obtain the missing knowledge from those sources; actually obtain the missing knowledge; and use the obtained(More)
The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePS-based agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as arguments of propositions and other expressions without leaving first-order(More)
Contextual Information is proving to be not only an additional exploitable information source for improving entity and situational estimates in certain Information Fusion systems, but can also be the entire focus of estimation for such systems as those directed to Ambient Intelligence (AI) and Context-Aware(CA) applications. This paper will discuss the(More)
A soft-information fusion process produces refined estimates of soft-information, such as natural language messages. Information resulting from a soft-information process can be used to retrieve related, relevant information from background (a-priori) knowledge sources using contextual “cues” contained in those messages, a process we call(More)
We present a categorization of contextual constraints, and discuss their uses in embodied agent architectures. “Context” has been described as a difficult term to define, because it’s: (1) used across numerous disciplines in cognitive science and computer science; (2) relative to an agent, or device; and (3) relative to the cognitive process being examined(More)