A Framework for Evaluating Early-Stage Human - of Marcus Hutter

More documents

Recommendations

Info

� a room has a floor � a room has a door � a room has a means of illumination � a room can contain a person � a table is a type of furniture � a family room is a type of room Discourse elaboration, via spreading activation, could add furniture, and subsequently table, to the discourse context by activating cached links derived from these rules. A later example will demonstrate. Pruning By Spreading Activation Analogous to how automatic speech recognizers operate, the number of retained interpretations in the search space is kept to a specified beam width (e.g. four retained interpretations). At the conclusion of utterance parsing, the highest scoring interpretation is returned as the result. An Example The following diagrams were produced during the processing of the example utterance: “the book is on the table“. As part of the experiment, the discourse context is primed with knowledge of a room, which is an instance of cyc:RoomInAConstruction, and a book, which is an instance of cyc:BookCopy. Lexical grammar rules matching word stems in the example utterance yield these ambiguous meanings: � book - a bound book copy � book - a sheath of paper, e.g. match book � is - has as an attribute � is - situation described as � on - an operational device � “on the table” - subject to negotiation [ a multiword word form ] � on - located on the surface of These concepts form nodes in a graph, whose links designate a conceptual relationship between two concepts. Marker-passing spreading activation originates at the known discourse terms (e.g. cyc:RoomInAConstruction) and at each significant utterance term (e.g. cyc:Table) and terminates if paths meet (e.g. at cyc:FurniturePiece). When it can be inferred in this fashion that an utterance term and a known discourse term are conceptually related, then that proposition is added to the meaning propositions for subsequent Kintsch spreading activation to resolve ambiguities. The marker-passing spreading activation decays after only a few links to preclude weakly conceptually related results. The Texai dialog system maintains a tree of parsing interpretation nodes. Each node in the tree is either an input word, such as ‘the’, or the name of an applied fluid construction grammar rule. Branches in this tree occur when there are alternative interpretations (i.e. meanings) for a word such as “book“. The parsing interpretation tree 159 is retained after the parsing process completes so that the user can ask questions about the parsing state (e.g. why a certain grammar rule did not apply as expected). Figure 2. Two alternative interpretations of “book” Figure 2 depicts the tree of two alternative parsing interpretations for the partial utterance “the book” whose leaves are: node 16 which represents an instance of cyc:BookCopy, and node 17 which represents an instance of texai:SheetsBoundTogetherOnOneEdge. Quoted nodes in the tree represent incrementally parsed words, and the remaining nodes name the applied grammar rule. According to Walter Kintsch’s theory of reading comprehension, spreading activation flows over the nodes of a graph formed by the meaning propositions of the utterance. Links in this graph connect nodes mentioning the same term. The most relevant set of nodes receives the highest activation. In figure 2 below are the ten propositions from the alternative parsing interpretations of the phrase “the book“. In the corresponding figure 3, magenta colored nodes indicate the interpretation: SheetsBoundTogetherOnOneEdge, Cyan colored nodes indicated the alternative interpretation cyc:BookCopy. The yellow nodes indicates prior knowledge - N4 is the prior discourse context knowledge about a cyc:Table, and N5 is the prior discourse context knowledge about a cyc:BookCopy. N1 and N7 are positively connected, which is indicated by a black line, because they share the concept: SheetsBoundTogetherOnOneEdge-2. Node N1 and N10 are negatively connected, which is indicated by a red line, because they represent alternative, conflicting, interpretations.
node RDF proposition N1 N2 N3 N4 N5 N6 N7 N8 N9 [texai:SheetsBoundTogetherOnOneEdge-2 texai:fcgStatus texai:SingleObject] [texai:BookCopy-1 rdf:type cyc:BookCopy)] [texai:BookCopy-1 texai:fcgDiscourseRole texai:external] [texai:table-0 rdf:type cyc:Table] [texai:book-0 rdf:type cyc:BookCopy] [texai:BookCopy-1 rdf:type texai:PreviouslyIntroducedThingInThisDisco urse] [texai:SheetsBoundTogetherOnOneEdge-2 rdf:type texai:PreviouslyIntroducedThingInThisDisco urse] [texai:SheetsBoundTogetherOnOneEdge-2 rdf:type texai:SheetsBoundTogetherOnOneEdge] [texai:SheetsBoundTogetherOnOneEdge-2 texai:fcgDiscourseRole texai:external] N10 [texai:BookCopy-1 texai:fcgStatus texai:SingleObject] Figure 3. RDF Propositions From Two Alternative Interpretations Figure 4. Quiesced Kintsch Spreading Activation Graph Incremental Generation The dialog system performs incremental utterance generation. Presently, the dialog planner is rudimentary, and consists of a component that forms a semantic dependence tree from terms in the set of propositions to be communicated to the user. The RDF propositions are gathered by their RDF subject term. One of the terms is heuristically chosen to be the subject of the utterance. Each of the propositions having this term as an RDF subject is selected for the root semantic dependency node. Child nodes are likewise created heuristically for the remaining propositions, grouped by RDF subject term. Incremental generation proceeds in much the same fashion as incremental parsing due to the fact that FCG is bi- 160 directional. As rules match, the resulting utterance is generated left-to-right, word by word. Whenever no rules match, the propositions from the next semantic dependency node are added to the top unit WM feature structure. Pruning of alternative interpretations will be a future research issue. Currently, simple scoring heuristics are: • prefer fewer words • prefer to reuse previously uttered words for a given meaning term • prefer to use words that the recipient is otherwise likely to know Finally, the resulting generated utterance is trial parsed to ensure that the system can understand what it generates with respect to the discourse context and its model of the user's belief state. Vocabulary Acquisition Once the most basic English grammar constructions are hand-coded, it is planed that Texai learn, by being taught, the constructions required for it to comprehend the word sense glosses (i.e. definitions) from WordNet and Wiktionary. By converting this definitional text into crisp, symbolic logic statements, Texai will acquire a degree of commonsense understanding about the defined concepts. The author is postponing grounding most of these concepts in physical perceptions. Initially, the only fully grounded symbols will be those involved with English grammar constructions and vocabulary. That is, Texai will have a grounded perception of what an utterance is, because it can directly sense one and it can generate one. It will likewise have a grounded notion of its mentors. It will be able to sense what each one says to it, and to influence their belief state by what it says. It might be considered circular and shallow to define the various dictionary words in terms of each other. But this will be sufficient for question answering - e.g. better than an existing search engine on the same corpus. And one might reasonably expect that it will be sufficient for the really important step to soon follow - the acquisition of skills by being taught them. Grammar Rule Acquisition At first, rather simple, stylized dialogs will handle idioms and multi-word forms by requesting their corresponding meaning from the user. It remains the subject of future experimentation to find the best way to input logical formulas. The initial idea will be to accept RDF syntax. After a sufficient vocabulary for describing FCG constructions is developed, then Texai can be taught additional complex grammar rules. This facility will be preceded by a sub-facility that will enable the dialog system to describe to its mentor why a grammar rule succeeded or failed for a particular parsed or generated utterance.
Page 2 and 3:
Ben Goertzel, Pascal Hitzler, Marcu
Page 4 and 5:
Artificial General Intelligence Vol
Page 6:
Preface Artificial General Intellig
Page 9 and 10:
Organizing Committee Tsvi Achler U.
Page 11 and 12:
A formal framework for the symbol g
Page 13 and 14:
Importing Space-time Concepts Into
Page 15 and 16:
one structure, everything else adap
Page 17 and 18:
generalize is essential to understa
Page 19 and 20:
First Application The above framewo
Page 21 and 22:
problem solving, use of knowledge,
Page 23 and 24:
Of particular note is the separatio
Page 25 and 26:
Conclusions and Future Work While p
Page 27 and 28:
Conceptual Spaces The conceptual ar
Page 29 and 30:
perceptions and actions. The lingui
Page 31 and 32:
means of the knowledge stored in th
Page 33 and 34:
grams given some (syntactically res
Page 35 and 36:
For example, let reverse([]) = [] r
Page 37 and 38:
Igor2 produced solutions with auxil
Page 39 and 40:
evolve neural net modules as quickl
Page 41 and 42:
ain”, consisting of some dozen or
Page 43 and 44:
Population Chr NListPtr m Chrm is a
Page 45 and 46:
and shaping, we feel that exploring
Page 47 and 48:
Table 2 reviews the key capabilitie
Page 49 and 50:
One way to achieve this goal would
Page 51 and 52:
question. Our approach of staying a
Page 53 and 54:
H0 δB(H0) δF(H0) H1 δB(H1) δF(H
Page 55 and 56:
Discussion and future work Testing
Page 57 and 58:
Types of Reasoning Corresponding Fo
Page 59 and 60:
Integrating Different Types of Reas
Page 61 and 62:
good overview of analogy models can
Page 63 and 64:
��
Page 65 and 66:
� ��
Page 67 and 68:
��
Page 69 and 70:
A Unified Framework for IP Conditio
Page 71 and 72:
negative evidence when added to a r
Page 73 and 74:
ing strategy. Due to GOLEM’s rand
Page 75 and 76:
any state index, n∈IN the current
Page 77 and 78:
is the best observation summary, wh
Page 79 and 80:
to a code for the rewards only, whi
Page 81 and 82:
have exponentially many states (2O(
Page 83 and 84:
where ˆσ 2 =Loss( ˆw)/n. Given
Page 85 and 86:
• The cost function can be improv
Page 87 and 88:
decides to introduce inflation or d
Page 89 and 90:
€ € The diffusion matrix is the
Page 91 and 92:
tuning may be done adaptively by te
Page 93 and 94:
Of course, Harnad’s argument is n
Page 95 and 96:
By exploring variations of Harnad
Page 97 and 98:
Discussion The representational sys
Page 99 and 100:
the cognitive map is less of a map
Page 101 and 102:
models of cognition except in cases
Page 103 and 104:
7(b), we can see that the straight
Page 105 and 106:
eaction times, error rates, or exac
Page 107 and 108:
comparing behavior with and without
Page 109 and 110:
Doorenbos, R. B. 1994. Combining le
Page 111 and 112:
egion, we leave the type of object
Page 113 and 114:
extract features in support of reco
Page 115 and 116:
with a minimum score of -1.0. The
Page 117 and 118:
the NASA Human Error Modeling compa
Page 119 and 120:
whether their models are capable of
Page 121 and 122: Incorporating Planning and Reasonin
Page 123 and 124: see an unclaimed ten-dollar bill al
Page 125 and 126: swering user queries) and the state
Page 127 and 128: Program Representation for General
Page 129 and 130: distribution P corresponding to the
Page 131 and 132: with all Ei replaced by the unbound
Page 133 and 134: Consciousness in Human and Machine:
Page 135 and 136: at the sensory receptors, is pre-pr
Page 137 and 138: The second choice is to take a deta
Page 139 and 140: Hebbian Constraint on the Resolutio
Page 141 and 142: The Case of Inputs that Change by T
Page 143 and 144: This route is relevant if the noise
Page 145 and 146: 1. Oculus Info. Inc. Toronto, Ont.
Page 147 and 148: Our implemented Turing judge used a
Page 149 and 150: 2.7; Human vs. JabberWacky t(157.6)
Page 151 and 152: UnsupervisedSegmentationofAudioSpee
Page 153 and 154: FinallyweusedthediscreteFourierTran
Page 155 and 156: Secondly,thereexistsmorethanonelogi
Page 157 and 158: Parsing PCFG within a General Proba
Page 159 and 160: known in advance, although many imp
Page 161 and 162: Figure 2: Shows the underlying weig
Page 163 and 164: Self-Programming: Operationalizing
Page 165 and 166: expressivity. More over, self-progr
Page 167 and 168: existence of a distance function ov
Page 169 and 170: Bootstrap Dialog: A Conversational
Page 171: elation is typically a verb. In the
Page 175 and 176: Analytical Inductive Programming as
Page 177 and 178: Problem domain: puttable(x) PRE: cl
Page 179 and 180: eq Hanoi(0, Src, Aux, Dst, S) = mov
Page 181 and 182: Human and Machine Understanding Of
Page 183 and 184: case orderings t correspond to the
Page 185 and 186: in (1) received a different denotat
Page 187 and 188: Abstract This paper analyzes the di
Page 189 and 190: Having grounded meaning: In an inte
Page 191 and 192: to experience” can be argued to b
Page 193 and 194: Abstract Case-by-case Problem Solvi
Page 195 and 196: First, since NARS is designed in th
Page 197 and 198: typical response is to find such an
Page 199 and 200: What Is Artificial General Intellig
Page 201 and 202: Finally, the facts that both seem t
Page 203 and 204: differ. NARS uses Narsese, the fair
Page 205 and 206: Integrating Action and Reasoning th
Page 207 and 208: states, with the condition that the
Page 209 and 210: action model. The system is able to
Page 211 and 212: Neuroscience and AI Share the Same
Page 213 and 214: Relevance Based Planning: Why Its a
Page 215 and 216: General Intelligence and Hypercompu
Page 217 and 218: To appear, AGI-09 1 Stimulus proces
Page 219 and 220: Distribution of Environments in For
Page 221 and 222: The Importance of Being Neural-Symb
Page 223 and 224:
Improving the Believability of Non-
Page 225 and 226:
Understanding the Brain’s Emergen
Page 227 and 228:
Abstract The challenge of creating
Page 229 and 230:
Importing Space-time Concepts Into
Page 231 and 232:
HELEN: Using Brain Regions and Mech
Page 233 and 234:
Holistic Intelligence: Transversal
Page 235 and 236:
Achieving Artificial General Intell
Page 237:
Achler, Tsvi . . . . . . . . . . .
show all

A Framework for Evaluating Early-Stage Human - of Marcus Hutter

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?