Steps Towards Building a Story Understanding Engine
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Poster presented at the 14th International Conference on Principles of Knowledge Representation and Reasoning, 20-24 July 2014 – Vienna, Austria
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![Develop an engine that can understand
stories like Humans do.
Objective “Knowledge Coder” – GWAP
We adopt the use of Games with A Purpose (GWAPs) for the
crowdsourcing of knowledge acquisition as a way of motivating
people to participate. “Knowledge Coder” game was developed.
Steps Towards Building a Story Understanding Engine
Christos T. Rodosthenous and Loizos Michael
Open University of Cyprus, Computational Cognition Lab
Knowledge Representation
References
[1] Irene-Anna Diakidoy, Antonis Kakas, Loizos
Michael, and Rob Miller. Story Comprehension
through Argumentation. In Proceedings of the
5th International Conference on Computational
Models of Argument (COMMA’14), Scottish
highlands, UK, 2014.
[2] Loizos Michael. Computability of Narrative. In
Proceedings of the 2nd Symposium on
Computational Models of Narrative (CMN’10),
Arlington, Virginia, USA, 2010.
Convert stories to formal
representation
Reason by integrating story
information with background
knowledge
Gather background knowledge and
represent it formally
High-level version of the Event Calculus [2].
Φ implies L
Φ causes L
e.g., person(X) implies can(X,think)
Tool for reasoning and visualizing a
comprehension model.
e.g., attack(X,Y) causes war(X,Y)
Join the Earth resistance forces by registering on the “Knowledge Coder”
game. The game is accessible online using any modern web browser at:
Join our efforts to acquire background knowledge
https://cognition.ouc.ac.cy/narrative/
Story snippet: A cat chased the mice. The mice managed to
hide in a nearby hole.
A cat chased
the mice. cat,
chase,
mouse
Methodology and Tools for knowledge
acquisition, representation, reasoning
and question answering.
Knowledge Gathering Experimental Results
Applicability (the conditions in the body of the rule are
met in the context of the selected sentence )
Validity (the head of the rule follows from the selected
sentence)
Experimental Output
Number of rules generated 93
Number of causality rules 15
Number of implication rules 78
Verbs chase
Nouns cat,mouse
1
chase(cat,mouse) causes fear(mouse,cat)
chase(cat,mouse) implies can(cat,run)
chase(cat,mouse)
cat(X) and chase(X,Y) implies can(X,run)
chase(X,Y) implies can(cat,run)
Background knowledge gathered from our developed game
offers some initial encouraging results in terms of the feasibility
of our methodology. More experiments are needed though.
Ongoing and Future Work
Extend “Knowledge Coder” with new “mission” for rule
preference selection.
Integrate “Knowledge Coder” with reasoning module.
Rule 1: beast(X) and throw(Y,mouth,X) implies kill(X,Y)
Rule 2: beast(X) and man(Y) and doe(Z) and exclaime(Z)
and escape(Z,Y) and throw(Z,X) implies kill(X,Z)
Rule preference for conflict resolution.
Move towards a more psychologically oriented comprehension
reasoning module [1].
Typos are
common in
GWAPs.
Solutions?
Experimental Setup
Number of participants 5
Number of Aesop's Fables 2
Contact
URL: http://cognition.ouc.ac.cy
1
2
3
2
3
4
5
6
7
Develop a module for converting stories to formal representations.
2
3
Applying
preferred rule.
Email: christos.rodosthenous@ouc.ac.cy](https://image.slidesharecdn.com/kr14poster-160625095344/85/Steps-Towards-Building-a-Story-Understanding-Engine-1-320.jpg)
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The document discusses an educational learning framework that can be used for context-rich simulations and games. It focuses on developing students' declarative, procedural, and strategic knowledge through knowledge-centered, learner-centered, and community-centered approaches. Key aspects of the framework include reflecting on experiences to improve practice, developing competencies through assessment, and applying knowledge through challenge-based learning activities within an interpretive community.
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The document discusses different types of artificial intelligence and outlines a new project to install the ACT-R cognitive architecture onto a NAO robot to create a robot with human-level general intelligence and flexible goal-directed behavior through embodied cognition, perception, motor skills, communication, learning and adaptation. The goal is to gain insights into building advanced autonomous agents by modeling key aspects of human cognition and intelligence.C463_01_intro.ppt
This document outlines the course content for an introduction to artificial intelligence class. It will cover topics such as the definition of AI, intelligent agents, logic and knowledge representation, machine learning algorithms like neural networks and genetic algorithms, and elements of natural language processing. The course will also discuss visions of AI like systems that think or act rationally or like humans. It provides historical context on the development of the field and successes in AI.
C463_01_intro.ppt
This document outlines the course for an Artificial Intelligence class. It introduces topics like intelligent agents, logic, knowledge representation, reasoning, machine learning, and natural language processing. It also discusses definitions of artificial and natural intelligence and different visions of AI like systems that think or act like humans versus those that think or act rationally. The history of AI is covered from early developments in neural networks and problem solving systems to more recent successes in games, robotics, and commercial applications.
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1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
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• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
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Please download this presentation to enjoy the hyperlinks!
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Steps Towards Building a Story Understanding Engine
- 1. Develop an engine that can understand stories like Humans do. Objective “Knowledge Coder” – GWAP We adopt the use of Games with A Purpose (GWAPs) for the crowdsourcing of knowledge acquisition as a way of motivating people to participate. “Knowledge Coder” game was developed. Steps Towards Building a Story Understanding Engine Christos T. Rodosthenous and Loizos Michael Open University of Cyprus, Computational Cognition Lab Knowledge Representation References [1] Irene-Anna Diakidoy, Antonis Kakas, Loizos Michael, and Rob Miller. Story Comprehension through Argumentation. In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA’14), Scottish highlands, UK, 2014. [2] Loizos Michael. Computability of Narrative. In Proceedings of the 2nd Symposium on Computational Models of Narrative (CMN’10), Arlington, Virginia, USA, 2010. Convert stories to formal representation Reason by integrating story information with background knowledge Gather background knowledge and represent it formally High-level version of the Event Calculus [2]. Φ implies L Φ causes L e.g., person(X) implies can(X,think) Tool for reasoning and visualizing a comprehension model. e.g., attack(X,Y) causes war(X,Y) Join the Earth resistance forces by registering on the “Knowledge Coder” game. The game is accessible online using any modern web browser at: Join our efforts to acquire background knowledge https://cognition.ouc.ac.cy/narrative/ Story snippet: A cat chased the mice. The mice managed to hide in a nearby hole. A cat chased the mice. cat, chase, mouse Methodology and Tools for knowledge acquisition, representation, reasoning and question answering. Knowledge Gathering Experimental Results Applicability (the conditions in the body of the rule are met in the context of the selected sentence ) Validity (the head of the rule follows from the selected sentence) Experimental Output Number of rules generated 93 Number of causality rules 15 Number of implication rules 78 Verbs chase Nouns cat,mouse 1 chase(cat,mouse) causes fear(mouse,cat) chase(cat,mouse) implies can(cat,run) chase(cat,mouse) cat(X) and chase(X,Y) implies can(X,run) chase(X,Y) implies can(cat,run) Background knowledge gathered from our developed game offers some initial encouraging results in terms of the feasibility of our methodology. More experiments are needed though. Ongoing and Future Work Extend “Knowledge Coder” with new “mission” for rule preference selection. Integrate “Knowledge Coder” with reasoning module. Rule 1: beast(X) and throw(Y,mouth,X) implies kill(X,Y) Rule 2: beast(X) and man(Y) and doe(Z) and exclaime(Z) and escape(Z,Y) and throw(Z,X) implies kill(X,Z) Rule preference for conflict resolution. Move towards a more psychologically oriented comprehension reasoning module [1]. Typos are common in GWAPs. Solutions? Experimental Setup Number of participants 5 Number of Aesop's Fables 2 Contact URL: http://cognition.ouc.ac.cy 1 2 3 2 3 4 5 6 7 Develop a module for converting stories to formal representations. 2 3 Applying preferred rule. Email: christos.rodosthenous@ouc.ac.cy