Knowledge Reusing Neural Learning System for Immediate Adaptation in Navigation Tasks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Akitoshi Ogawa , Takashi Omori

ABSTRACT

A characteristic feature of conventional intelligent agents is the amount of trials that are required for them to learn. Since the tasks they encounter change depending on the environment, it is difficult for a learning system to compress the learning time using a priori knowledge. In the real world, however, agents confront a whole range of new tasks one by one, and have to solve them one by one without consuming learning time. A serious problem for the real-world agent is the amount of learning time needed. We suppose that one reason for a long learning time is the nonuse of prior knowledge. It is natural to expect that a kind of fast adaptation to tasks would be possible when we reuse knowledge that is acquired from similar past experiences. For this problem, we propose a neural-network based learning system that can immediately or quickly solve new tasks by reusing knowledge already acquired. We adopt a navigation task as an example and show the effectiveness of our method in variations on the task by comparing its performance with other methods. More... »

PAGES

409-415

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-30499-9_62

DOI

http://dx.doi.org/10.1007/978-3-540-30499-9_62

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1000979818


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ogawa", 
        "givenName": "Akitoshi", 
        "id": "sg:person.016531377655.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016531377655.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Omori", 
        "givenName": "Takashi", 
        "id": "sg:person.01263557346.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01263557346.07"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2004", 
    "datePublishedReg": "2004-01-01", 
    "description": "A characteristic feature of conventional intelligent agents is the amount of trials that are required for them to learn. Since the tasks they encounter change depending on the environment, it is difficult for a learning system to compress the learning time using a priori knowledge. In the real world, however, agents confront a whole range of new tasks one by one, and have to solve them one by one without consuming learning time. A serious problem for the real-world agent is the amount of learning time needed. We suppose that one reason for a long learning time is the nonuse of prior knowledge. It is natural to expect that a kind of fast adaptation to tasks would be possible when we reuse knowledge that is acquired from similar past experiences. For this problem, we propose a neural-network based learning system that can immediately or quickly solve new tasks by reusing knowledge already acquired. We adopt a navigation task as an example and show the effectiveness of our method in variations on the task by comparing its performance with other methods.", 
    "editor": [
      {
        "familyName": "Pal", 
        "givenName": "Nikhil Ranjan", 
        "type": "Person"
      }, 
      {
        "familyName": "Kasabov", 
        "givenName": "Nik", 
        "type": "Person"
      }, 
      {
        "familyName": "Mudi", 
        "givenName": "Rajani K.", 
        "type": "Person"
      }, 
      {
        "familyName": "Pal", 
        "givenName": "Srimanta", 
        "type": "Person"
      }, 
      {
        "familyName": "Parui", 
        "givenName": "Swapan Kumar", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-30499-9_62", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-23931-4", 
        "978-3-540-30499-9"
      ], 
      "name": "Neural Information Processing", 
      "type": "Book"
    }, 
    "keywords": [
      "learning system", 
      "learning time", 
      "navigation task", 
      "long learning time", 
      "neural learning system", 
      "real-world agents", 
      "intelligent agents", 
      "amount of trial", 
      "similar past experiences", 
      "real world", 
      "new tasks", 
      "prior knowledge", 
      "task", 
      "task one", 
      "fast adaptation", 
      "past experience", 
      "system", 
      "immediate adaptation", 
      "knowledge", 
      "serious problem", 
      "environment", 
      "adaptation", 
      "performance", 
      "method", 
      "effectiveness", 
      "time", 
      "features", 
      "experience", 
      "kind", 
      "example", 
      "amount", 
      "world", 
      "problem", 
      "one", 
      "agents", 
      "nonuse", 
      "trials", 
      "reasons", 
      "changes", 
      "whole range", 
      "characteristic features", 
      "range", 
      "variation", 
      "conventional intelligent agents", 
      "new tasks one", 
      "Knowledge Reusing Neural Learning System", 
      "Reusing Neural Learning System"
    ], 
    "name": "Knowledge Reusing Neural Learning System for Immediate Adaptation in Navigation Tasks", 
    "pagination": "409-415", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1000979818"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-30499-9_62"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-30499-9_62", 
      "https://app.dimensions.ai/details/publication/pub.1000979818"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:06", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_0.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-30499-9_62"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30499-9_62'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30499-9_62'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30499-9_62'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-30499-9_62'


 

This table displays all metadata directly associated to this object as RDF triples.

142 TRIPLES      23 PREDICATES      75 URIs      66 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-30499-9_62 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:17
4 anzsrc-for:1701
5 schema:author N88da9994e11445fc88065401afe07cab
6 schema:datePublished 2004
7 schema:datePublishedReg 2004-01-01
8 schema:description A characteristic feature of conventional intelligent agents is the amount of trials that are required for them to learn. Since the tasks they encounter change depending on the environment, it is difficult for a learning system to compress the learning time using a priori knowledge. In the real world, however, agents confront a whole range of new tasks one by one, and have to solve them one by one without consuming learning time. A serious problem for the real-world agent is the amount of learning time needed. We suppose that one reason for a long learning time is the nonuse of prior knowledge. It is natural to expect that a kind of fast adaptation to tasks would be possible when we reuse knowledge that is acquired from similar past experiences. For this problem, we propose a neural-network based learning system that can immediately or quickly solve new tasks by reusing knowledge already acquired. We adopt a navigation task as an example and show the effectiveness of our method in variations on the task by comparing its performance with other methods.
9 schema:editor Ndae73f1a644e40c8b43460b05884c547
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N3bf57a8195a4447699fa72b9cf233ad1
14 schema:keywords Knowledge Reusing Neural Learning System
15 Reusing Neural Learning System
16 adaptation
17 agents
18 amount
19 amount of trial
20 changes
21 characteristic features
22 conventional intelligent agents
23 effectiveness
24 environment
25 example
26 experience
27 fast adaptation
28 features
29 immediate adaptation
30 intelligent agents
31 kind
32 knowledge
33 learning system
34 learning time
35 long learning time
36 method
37 navigation task
38 neural learning system
39 new tasks
40 new tasks one
41 nonuse
42 one
43 past experience
44 performance
45 prior knowledge
46 problem
47 range
48 real world
49 real-world agents
50 reasons
51 serious problem
52 similar past experiences
53 system
54 task
55 task one
56 time
57 trials
58 variation
59 whole range
60 world
61 schema:name Knowledge Reusing Neural Learning System for Immediate Adaptation in Navigation Tasks
62 schema:pagination 409-415
63 schema:productId N2c6956c4ccfb4c73a4efb9de5387d826
64 Ne38a702c1190449f9f4692fcc08c9fec
65 schema:publisher Nc6356395805d47ccbb4099d4ffd8d680
66 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000979818
67 https://doi.org/10.1007/978-3-540-30499-9_62
68 schema:sdDatePublished 2022-01-01T19:06
69 schema:sdLicense https://scigraph.springernature.com/explorer/license/
70 schema:sdPublisher N5113502f18db49b1ac8d47a1c79cf72c
71 schema:url https://doi.org/10.1007/978-3-540-30499-9_62
72 sgo:license sg:explorer/license/
73 sgo:sdDataset chapters
74 rdf:type schema:Chapter
75 N1b548a6a09554c268f76b1dc5684f618 rdf:first sg:person.01263557346.07
76 rdf:rest rdf:nil
77 N2c6956c4ccfb4c73a4efb9de5387d826 schema:name dimensions_id
78 schema:value pub.1000979818
79 rdf:type schema:PropertyValue
80 N3bf57a8195a4447699fa72b9cf233ad1 schema:isbn 978-3-540-23931-4
81 978-3-540-30499-9
82 schema:name Neural Information Processing
83 rdf:type schema:Book
84 N4466d4a5ae024d56a7d89a6fc90a4b94 schema:familyName Mudi
85 schema:givenName Rajani K.
86 rdf:type schema:Person
87 N5113502f18db49b1ac8d47a1c79cf72c schema:name Springer Nature - SN SciGraph project
88 rdf:type schema:Organization
89 N88da9994e11445fc88065401afe07cab rdf:first sg:person.016531377655.12
90 rdf:rest N1b548a6a09554c268f76b1dc5684f618
91 N963169202f8f45879eec1366a9729c32 schema:familyName Pal
92 schema:givenName Srimanta
93 rdf:type schema:Person
94 Na41daecc70cd49f5b2c673edf0b6f6a3 rdf:first Nf155023303804430ae39da2c5e6fb506
95 rdf:rest Nd2824120a7ad4b3298b30beb3fa394bf
96 Na58dc825403b4bec83449c428079aba0 rdf:first Nf37c418207a6434b9d1be8bdab8666bf
97 rdf:rest rdf:nil
98 Nc6356395805d47ccbb4099d4ffd8d680 schema:name Springer Nature
99 rdf:type schema:Organisation
100 Nd2824120a7ad4b3298b30beb3fa394bf rdf:first N4466d4a5ae024d56a7d89a6fc90a4b94
101 rdf:rest Nd2cfd90009cf415a822fcea7fb617249
102 Nd2cfd90009cf415a822fcea7fb617249 rdf:first N963169202f8f45879eec1366a9729c32
103 rdf:rest Na58dc825403b4bec83449c428079aba0
104 Ndae73f1a644e40c8b43460b05884c547 rdf:first Ne2d13f8a152243d0b4af255928fca0d4
105 rdf:rest Na41daecc70cd49f5b2c673edf0b6f6a3
106 Ne2d13f8a152243d0b4af255928fca0d4 schema:familyName Pal
107 schema:givenName Nikhil Ranjan
108 rdf:type schema:Person
109 Ne38a702c1190449f9f4692fcc08c9fec schema:name doi
110 schema:value 10.1007/978-3-540-30499-9_62
111 rdf:type schema:PropertyValue
112 Nf155023303804430ae39da2c5e6fb506 schema:familyName Kasabov
113 schema:givenName Nik
114 rdf:type schema:Person
115 Nf37c418207a6434b9d1be8bdab8666bf schema:familyName Parui
116 schema:givenName Swapan Kumar
117 rdf:type schema:Person
118 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
119 schema:name Information and Computing Sciences
120 rdf:type schema:DefinedTerm
121 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
122 schema:name Artificial Intelligence and Image Processing
123 rdf:type schema:DefinedTerm
124 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
125 schema:name Psychology and Cognitive Sciences
126 rdf:type schema:DefinedTerm
127 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
128 schema:name Psychology
129 rdf:type schema:DefinedTerm
130 sg:person.01263557346.07 schema:affiliation grid-institutes:grid.39158.36
131 schema:familyName Omori
132 schema:givenName Takashi
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01263557346.07
134 rdf:type schema:Person
135 sg:person.016531377655.12 schema:affiliation grid-institutes:grid.39158.36
136 schema:familyName Ogawa
137 schema:givenName Akitoshi
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016531377655.12
139 rdf:type schema:Person
140 grid-institutes:grid.39158.36 schema:alternateName Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan
141 schema:name Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan
142 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...