Goal directed qualitative reasoning with partial states:
Abstract: "This research explores the representational and computational complexities of qualitative reasoning about time-varying behavior. Traditional techniques employ qualitative simulation (QS) to compute envisionments (i.e. state-transition graphs) representing all possible behaviors. Unfo...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Urbana, Ill.
Univ.
|
| Schlagworte: | |
| Zusammenfassung: | Abstract: "This research explores the representational and computational complexities of qualitative reasoning about time-varying behavior. Traditional techniques employ qualitative simulation (QS) to compute envisionments (i.e. state-transition graphs) representing all possible behaviors. Unfortunately, QS exhaustively case-splits on all choices, regardless of specific task goals. It reasons with completely described states and explores every (ambiguous) future of each. In this thesis we introduce a new representation, called sufficient discriminatory envisionments (SUDE's), which addresses these problems. SUDE's discriminate the possible behavior space by whether the goal is possible, impossible, or inevitable from each state in that space. Our techniques for generating SUDE's strive to reason with the smallest state descriptions which are sufficient for making these discriminations. We present algorithms for generating SUDE's via a two-stage process. First, exhaustive regression sketches the space of possible paths between the initial and goal states. Second, we qualify these possible paths, identifying conditions under which the goal is impossible or inevitable and finding all possible transitions between these paths. We formulate Nature's regression operators in terms of minimal chunks of causality, exploiting the causal, compositional nature of Qualitative Process Theory models. We integrate continuity-based and minimality-based theories of change to support discontinuous change due to actions and modelling simplifications. We discuss our implementation of these techniques and our test examples in three domains, which we call ball-world, tank-world, and kitchen-world." |
| Beschreibung: | Zugl.: Urbana, Ill., Univ., Diss., 1994 |
| Beschreibung: | XII, 151 S. |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV010618685 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | t | ||
| 008 | 960219nuuuuuuuu m||| 00||| engod | ||
| 035 | |a (OCoLC)31750045 | ||
| 035 | |a (DE-599)BVBBV010618685 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91G | ||
| 082 | 0 | |a 510.78 | |
| 084 | |a DAT 706d |2 stub | ||
| 088 | |a UIUCDCS R 94 1850 | ||
| 088 | |a UILU ENG 94 1708 | ||
| 100 | 1 | |a De Coste, Dennis M. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Goal directed qualitative reasoning with partial states |c by Dennis Martin DeCoste |
| 246 | 1 | 3 | |a UIUCDCS R 94 1850 |
| 246 | 1 | 3 | |a UILU ENG 94 1708 |
| 246 | 1 | 3 | |a Goal-directed qualitative reasoning with partial states |
| 264 | 1 | |a Urbana, Ill. |b Univ. | |
| 300 | |a XII, 151 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Zugl.: Urbana, Ill., Univ., Diss., 1994 | ||
| 520 | 3 | |a Abstract: "This research explores the representational and computational complexities of qualitative reasoning about time-varying behavior. Traditional techniques employ qualitative simulation (QS) to compute envisionments (i.e. state-transition graphs) representing all possible behaviors. Unfortunately, QS exhaustively case-splits on all choices, regardless of specific task goals. It reasons with completely described states and explores every (ambiguous) future of each. In this thesis we introduce a new representation, called sufficient discriminatory envisionments (SUDE's), which addresses these problems. SUDE's discriminate the possible behavior space by whether the goal is possible, impossible, or inevitable from each state in that space. Our techniques for generating SUDE's strive to reason with the smallest state descriptions which are sufficient for making these discriminations. We present algorithms for generating SUDE's via a two-stage process. First, exhaustive regression sketches the space of possible paths between the initial and goal states. Second, we qualify these possible paths, identifying conditions under which the goal is impossible or inevitable and finding all possible transitions between these paths. We formulate Nature's regression operators in terms of minimal chunks of causality, exploiting the causal, compositional nature of Qualitative Process Theory models. We integrate continuity-based and minimality-based theories of change to support discontinuous change due to actions and modelling simplifications. We discuss our implementation of these techniques and our test examples in three domains, which we call ball-world, tank-world, and kitchen-world." | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Computer simulation | |
| 650 | 4 | |a Reasoning | |
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-007083852 | ||
Datensatz im Suchindex
| _version_ | 1804125093134073856 |
|---|---|
| any_adam_object | |
| author | De Coste, Dennis M. |
| author_facet | De Coste, Dennis M. |
| author_role | aut |
| author_sort | De Coste, Dennis M. |
| author_variant | c d m d cdm cdmd |
| building | Verbundindex |
| bvnumber | BV010618685 |
| classification_tum | DAT 706d |
| ctrlnum | (OCoLC)31750045 (DE-599)BVBBV010618685 |
| dewey-full | 510.78 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 510 - Mathematics |
| dewey-raw | 510.78 |
| dewey-search | 510.78 |
| dewey-sort | 3510.78 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02916nam a2200409 c 4500</leader><controlfield tag="001">BV010618685</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">960219nuuuuuuuu m||| 00||| engod</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)31750045</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV010618685</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">510.78</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 706d</subfield><subfield code="2">stub</subfield></datafield><datafield tag="088" ind1=" " ind2=" "><subfield code="a">UIUCDCS R 94 1850</subfield></datafield><datafield tag="088" ind1=" " ind2=" "><subfield code="a">UILU ENG 94 1708</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">De Coste, Dennis M.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Goal directed qualitative reasoning with partial states</subfield><subfield code="c">by Dennis Martin DeCoste</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">UIUCDCS R 94 1850</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">UILU ENG 94 1708</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">Goal-directed qualitative reasoning with partial states</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Urbana, Ill.</subfield><subfield code="b">Univ.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XII, 151 S.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Zugl.: Urbana, Ill., Univ., Diss., 1994</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Abstract: "This research explores the representational and computational complexities of qualitative reasoning about time-varying behavior. Traditional techniques employ qualitative simulation (QS) to compute envisionments (i.e. state-transition graphs) representing all possible behaviors. Unfortunately, QS exhaustively case-splits on all choices, regardless of specific task goals. It reasons with completely described states and explores every (ambiguous) future of each. In this thesis we introduce a new representation, called sufficient discriminatory envisionments (SUDE's), which addresses these problems. SUDE's discriminate the possible behavior space by whether the goal is possible, impossible, or inevitable from each state in that space. Our techniques for generating SUDE's strive to reason with the smallest state descriptions which are sufficient for making these discriminations. We present algorithms for generating SUDE's via a two-stage process. First, exhaustive regression sketches the space of possible paths between the initial and goal states. Second, we qualify these possible paths, identifying conditions under which the goal is impossible or inevitable and finding all possible transitions between these paths. We formulate Nature's regression operators in terms of minimal chunks of causality, exploiting the causal, compositional nature of Qualitative Process Theory models. We integrate continuity-based and minimality-based theories of change to support discontinuous change due to actions and modelling simplifications. We discuss our implementation of these techniques and our test examples in three domains, which we call ball-world, tank-world, and kitchen-world."</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Künstliche Intelligenz</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reasoning</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4113937-9</subfield><subfield code="a">Hochschulschrift</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-007083852</subfield></datafield></record></collection> |
| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV010618685 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:56:04Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007083852 |
| oclc_num | 31750045 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | XII, 151 S. |
| publishDateSort | 0000 |
| publisher | Univ. |
| record_format | marc |
| spelling | De Coste, Dennis M. Verfasser aut Goal directed qualitative reasoning with partial states by Dennis Martin DeCoste UIUCDCS R 94 1850 UILU ENG 94 1708 Goal-directed qualitative reasoning with partial states Urbana, Ill. Univ. XII, 151 S. txt rdacontent n rdamedia nc rdacarrier Zugl.: Urbana, Ill., Univ., Diss., 1994 Abstract: "This research explores the representational and computational complexities of qualitative reasoning about time-varying behavior. Traditional techniques employ qualitative simulation (QS) to compute envisionments (i.e. state-transition graphs) representing all possible behaviors. Unfortunately, QS exhaustively case-splits on all choices, regardless of specific task goals. It reasons with completely described states and explores every (ambiguous) future of each. In this thesis we introduce a new representation, called sufficient discriminatory envisionments (SUDE's), which addresses these problems. SUDE's discriminate the possible behavior space by whether the goal is possible, impossible, or inevitable from each state in that space. Our techniques for generating SUDE's strive to reason with the smallest state descriptions which are sufficient for making these discriminations. We present algorithms for generating SUDE's via a two-stage process. First, exhaustive regression sketches the space of possible paths between the initial and goal states. Second, we qualify these possible paths, identifying conditions under which the goal is impossible or inevitable and finding all possible transitions between these paths. We formulate Nature's regression operators in terms of minimal chunks of causality, exploiting the causal, compositional nature of Qualitative Process Theory models. We integrate continuity-based and minimality-based theories of change to support discontinuous change due to actions and modelling simplifications. We discuss our implementation of these techniques and our test examples in three domains, which we call ball-world, tank-world, and kitchen-world." Künstliche Intelligenz Artificial intelligence Computer simulation Reasoning (DE-588)4113937-9 Hochschulschrift gnd-content |
| spellingShingle | De Coste, Dennis M. Goal directed qualitative reasoning with partial states Künstliche Intelligenz Artificial intelligence Computer simulation Reasoning |
| subject_GND | (DE-588)4113937-9 |
| title | Goal directed qualitative reasoning with partial states |
| title_alt | UIUCDCS R 94 1850 UILU ENG 94 1708 Goal-directed qualitative reasoning with partial states |
| title_auth | Goal directed qualitative reasoning with partial states |
| title_exact_search | Goal directed qualitative reasoning with partial states |
| title_full | Goal directed qualitative reasoning with partial states by Dennis Martin DeCoste |
| title_fullStr | Goal directed qualitative reasoning with partial states by Dennis Martin DeCoste |
| title_full_unstemmed | Goal directed qualitative reasoning with partial states by Dennis Martin DeCoste |
| title_short | Goal directed qualitative reasoning with partial states |
| title_sort | goal directed qualitative reasoning with partial states |
| topic | Künstliche Intelligenz Artificial intelligence Computer simulation Reasoning |
| topic_facet | Künstliche Intelligenz Artificial intelligence Computer simulation Reasoning Hochschulschrift |
| work_keys_str_mv | AT decostedennism goaldirectedqualitativereasoningwithpartialstates AT decostedennism uiucdcsr941850 AT decostedennism uilueng941708 |