1 / 31

The Independent Choice Logic for modeling multiple agents under uncertainty

The Independent Choice Logic for modeling multiple agents under uncertainty. David Poole Http://www.cs.ubc.ca/spider/poole Presented by Mei Huang Mar. 18,2005. Outline. Knowledge Representation ICL Overview ICL Semantics ICL Influence Discussion. Outline. Knowledge Representation

kelsie-kidd
Télécharger la présentation

The Independent Choice Logic for modeling multiple agents under uncertainty

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Independent Choice Logic for modeling multiple agents under uncertainty David Poole Http://www.cs.ubc.ca/spider/poole Presented by Mei Huang Mar. 18,2005

  2. Outline • Knowledge Representation • ICL Overview • ICL Semantics • ICL Influence • Discussion

  3. Outline • Knowledge Representation • ICL Overview • ICL Semantics • ICL Influence • Discussion

  4. “a bridge between logical representation and belief networks”

  5. Q?

  6. Discussion • What do you think of the author’s claim that using disjunction to handle uncertainty is a stupid thing? • From the view point of representation, disjunction is not that bad, at least, it provides compact representations than ICL. E.g, “ea1v …v an” in ICL needs n expressions.

  7. Outline • Knowledge Representation • ICL Overview • ICL Semantics • ICL Influence • Discussion

  8. Note: each item in F, i.e, each rules has an associated probability. E.g, for the first rule, the probability is P(f | c1, b1). This probability is different from the probability of a possible world.

  9. Q?

  10. Discussion • How to cast the student-take-course domain problem into ICL? • Choice Space C={{L1,L2,L3},{High,Normal,Easy}} Facts F={Grade=A  IQ=L3 ^ Difficulty=Easy; Grade=B  IQ=L2 ^ Difficulty=High; ……} Give P0(L1), P0(L2), P0(L3), P0(High), P0(Normal), P0(Easy)

  11. Outline • Knowledge Representation • ICL Overview • ICL Semantics • ICL Influence • Discussion

  12. Q?

  13. Discussion • Why it looks so straight forward to map a decision tree to the rules of ICL? • The logical part of ICL focuses on attributes’ values, not attributes. ICL’s Fact set F interprets CPDs of BLP or LoPRM.

  14. Q?

  15. Discussion • How to translate this BN into ICL? • It is better to redraw the graph as 8 belief networks as follows: …… Because ICL’s rules are defined on the value level. Burglary earthQuake Alarm B=true Q=true A=true

  16. are mutually exclusive

  17. Outline • Knowledge Representation • ICL Overview • ICL Semantics • ICL Influence • Discussion

  18. Discussion • In the diagram in slide15, why the arrows are uni-directional?Can they be made bi-directional? • What can ICL buy us? I.e, is this explicit bridge between logic and decision theory necessary or is the gap between the two enlarged imaginarily by the author? (when we deal with decision projects, logic is usually embodied implicitly in the program)

More Related