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Architecturally, GRACE consists of perception, planning, and action subsystems [271]. GRACE s perception capability features speech and vision subsystems, but includes a laser range nder to assist in navigation and obstacle avoidance. Instead of a cognition cycle like CR1, control in GRACE is mediated by state changes in the perception and mobility subsystems. GRACE has very limited capability in dialog, typically getting stuck in shallow loops that reveal its lack of knowledge in commonsense domains [272]. CR1 s Hearsay serModel is a preprogrammed blank planning capability of CR1 for which stimuli and responses are learned during operation. GRACE generates plans from prede ned primitives, not from learned primitives. This showed when interacting with GRACE at AAAI 2004 [273]. At the time, GRACE could engage in small talk, but it was easily led into loops, asking the same question again without any apparent realization that it had just asked the same question. This isn t a criticism of GRACE per se. In other modes, GRACE is known to learn more but to be less predictable, which is a characteristic of the state of AML in 2004.
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In the search for abstractions by which sound and vision may be integrated, researchers have explored many knowledge and skill representation schemes. Frames, speech acts [12], and conceptual dependency [279] were seminal ideas that have been used for reasoning at a level of abstraction consistent with the <Dialog/> level of speech interaction and with the <Scene/> level of visual perception. Logic operating on frames can reason about possible worlds. Rules operating on frames can synthesize preprogrammed behavior. CBR over frames enables incremental knowledge acquisition. Conceptual dependency introduced PTRANS (physical transfer), ATRANS (change of attribute), and MTRANS (change of memory). LCS takes this formalization a step further. It characterizes words in terms of their contributions to abstract perception of things in space and time, states, and events [274]. LCS has a very small set of primitives by which lexical stimuli (words) bear spatial semantics at a high level of abstraction. The word room implies a place with an interior, a spatial mental model. Logic can describe possible worlds with terminals, predicates, quanti ers, and statements, but the predicates make most of the spatial knowledge unavailable except through additional statements like onTopof (Ball, table) inFrontof (Ball, clock). These abstract semantics are not lexical semantics because the meaning is not associated with the words, but with the larger frame structures. A <Scene/> of 10 objects requires a thousand (210) logic statements to fully express how the objects are oriented with respect to each other, a combinatorial explosion. With LCS, the <Scene/> is expressed in 10 statements to which words are referenced, deriving any of the thousand statements as needed. Speci cally, LCS annotates nouns as things (people or places that take up
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space); verbs as states, events, and causes; while prepositions characterize paths (Expression 10-2). Expression 10-2 Lexical Conceptual Semantics (LCS) Primitives
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[THING] [PLACE] <- PLACE-FUNC( [THING] ) PLACE-FUNC {AT, ON, IN, ABOVE, BELOW, BEHIND, . . .} [PATH] <- PATH-FUNC( [PLACE] | [THING] ) PATH-FUNC {TO, FROM, TOWARD, AWAY-FROM, VIA, ALONG, . . .} [EVENT] <- GO ([THING], [PATH]) | STAY ([THING], [PLACE]) [STATE] <- BE ([THING], [PLACE]) | ORIENT ([THING], [PATH]) | EXTEND ([THING], [PATH]) [CAUSE] <- CAUSE ([THING], [EVENT]) John Bender s software uses the LCS spatial characteristics of words to draw abstract pictures; related software parses the abstract pictures to generate sentential descriptions of the abstract pictures and to answer questions about the objects in the pictures [275]. The LCS expression for John walked into the room is a sequence of pictures with John on a path from a place outside the room to a place inside the room, denoted as follows: [Event GO ([Thing JOHN], [Path TO [Place IN [Thing ROOM]]])] (10-1)
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John is a <Thing/> as are all nouns in LCS and in RXML. The verb walked indicates a GO event, expressed in RXML as the user-domain knowledge that <Event> <Walked> GO </Walked> </Event>. The concepts of <Pathfunction/> and <Place-function/> give LCS its unique avor. <Path/> is the conceptual spatial analog of a locus of motion. Some prepositions like to and from express verbally the functional relationship between a <Place/> and a <Thing/> established computationally by <Path-function/> of LCS. <Place/> is a subset of space time that anchors <Place-function/>, the relationship between a <Place/> and a <Thing/>. These are expressed lexically by the position and orientation prepositions at, on, in, and so on. This forwardlooking research enables one to simply and accurately abstract the salient features of a <Scene/> and to relate <Dialog/> and <Scene/> to each other. Bender s programs, VISUALIZE and DESCRIBE, translate lexical LCS expressions to abstract drawings and conversely. RXML generalizes the <Place-function/> to a set of place-relations among places and things, such as <On> <Thing/> <Place/> </On>. The <Place/> something is on may be intimately associated with another <Thing/>, such as <Top/> of a <Table/>. Vision researchers have implemented similar performance with different methods, based on logical forms to describe scenes and changes in scenes, for example, so that a surveillance system can report anomalies such as the
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