'Answer set' diaporamas de présentation

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1.16k views • 97 slides

Information Retrieval

Information Retrieval. CSE 8337 (Part III) Spring 2011 Some Material for these slides obtained from: Modern Information Retrieval by Ricardo Baeza -Yates and Berthier Ribeiro-Neto http://www.sims.berkeley.edu/~hearst/irbook/

1.04k views • 83 slides

Predicate Answer Set Programming with Coinduction

Predicate Answer Set Programming with Coinduction. Dissertation Proposal by Richard Min. Department of Computer Science The University of Texas at Dallas, Richardson, Texas, USA. 1. Introduction. Answer Set Programming (ASP)

945 views • 78 slides

Information Retrieval

Information Retrieval. CSE 8337 Spring 2003 Modeling Material for these slides obtained from: Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto http://www.sims.berkeley.edu/~hearst/irbook/

674 views • 55 slides

Information Retrieval

Information Retrieval. CSE 8337 Spring 2005 Modeling Material for these slides obtained from: Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto http://www.sims.berkeley.edu/~hearst/irbook/

702 views • 55 slides

Part 7: Rules and Ontologies

Part 7: Rules and Ontologies. Combining rules and ontologies. We now know how to represent (possibly incomplete, evolving, etc) knowledge using rules, but assuming that the ontology is known. We also learned how to represent ontologies. The close the circle, we need to combine both.

520 views • 40 slides

Learning by Answer Sets

Learning by Answer Sets. Chiaki Sakama Wakayama University, Japan. Presented at AAAI Spring Symposium on Answer Set Programming, March 2001. Inductive Logic Programming (ILP). Goal : Inductive construction of first-order clausal theories from examples and background knowledge.

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Assertion Answer Set Logic Program and its Application

Assertion Answer Set Logic Program and its Application. Mingyi Zhang Guizhou Academy of Science 2012-6-18. Motivation. Motivation. (1) Lack of intuition Noncumulativity (Brewka,1991; Giorddano and Martelli,1994) Cause : Inconsistency between justification of defaults

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Default and Cooperative Reasoning in Multi-Agent Systems

Default and Cooperative Reasoning in Multi-Agent Systems. Chiaki Sakama Wakayama University, Japan. Programming Multi-Agent Systems based on Logic Dagstuhl Seminar, November 2002. Incomplete Knowledge in Multi-Agent System (MAS). An individual agent has incomplete knowledge in an MAS.

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ISP 433/533 Week 2

ISP 433/533 Week 2. IR Models. Outline. IR defined IR tasks IR processes Boolean model Break Vector space model Probabilistic model. User Information Needs. Goal of IR Hard Problem People have different and highly varied needs for information

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Coordination between Logical Agents

Coordination between Logical Agents. Chiaki Sakama Wakayama University Katsumi Inoue National Institute of Informatics CLIMA-V September 2004. Coordination in Multi-Agent Systems.

365 views • 28 slides

Answer set programming

Answer set programming. See Eiter et al 2009. Motivation: strong negation. Programme: man(dilbert). single(X) :- man(X), not husband(X). husband(X) :- man(X), not single(X). Expressing negative fact: - single(dilbert). Motivation: strong negation (2). Negation as failure :

446 views • 25 slides

Answer set programming

Answer set programming. See Eiter et al 2009. Motivation: strong negation. Programme: man(dilbert). single(X) :- man(X), not husband(X). husband(X) :- man(X), not single(X). Expressing negative fact: - single(dilbert). Motivation: strong negation (2). Negation as failure :

366 views • 25 slides

Co-inductive logic programming and its applications

Co-inductive logic programming and its applications. Overview. Induction vs. co-induction Co-inductive logic programming A goal-directed approach for Answer Set Computing. Induction. Inductive definitions have 3 components: Initiality (e.g., [] is a list)

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Software Measurement

Software Measurement. Big and Lack of Cohesion. Representational TOM. empirical relation system (C,R) numerical relation system (N,P) M maps (C,R) to (N,P) representation condition x<y => M(x)<M(y). BIG empirical. We want to measure people as BIG i.e. height and weight

259 views • 17 slides

Software Measurement

Software Measurement. Big and Lack of Cohesion. Representational TOM. empirical relation system (C,R) numerical relation system (N,P) M maps (C,R) to (N,P) representation condition x<y => M(x)<M(y). BIG empirical. We want to measure people as BIG i.e. height and weight

249 views • 16 slides

LIS618 lecture 6

LIS618 lecture 6. Thomas Krichel 2003-10-26. Structure. Probabilistic model News from the front line Open WorldCat Pilot Amazon Search Inside the book. probabilistic model ( outline only ). starts with the assumption that there is a subset of documents that form the ideal answer set

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Suppression Task

Suppression Task. Claudia van Kruistum December 12 th , 2005. Questions. What is the pattern of results? Are subjects good at reasoning, i.e. are they consistent? Is there any influence of the experimental setup on the data? If so, what?. Materials & Methods. 4 subjects, taped on video

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Learning by Answer Sets

Learning by Answer Sets. Chiaki Sakama Wakayama University, Japan. Presented at AAAI Spring Symposium on Answer Set Programming, March 2001. Nonmonotonic LP (NMLP) vs. Inductive LP (ILP). Purpose of Research. NMLP and ILP have different goals and complement each other.

209 views • 12 slides

Decision-Making on Robots U sing POMDPs and Answer Set Programming

Decision-Making on Robots U sing POMDPs and Answer Set Programming. Christian Washington Louisiana State University Department of Electrical and Computer Engineering. Shiqi Zhang, Mohan Sridharan Texas Tech University Department of Computer Science. Answer Set Programming. Introduction

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