The document discusses reasoning about actions and domains using logical formulas. It describes how to represent actions, their effects, executability, and domain constraints. The goal is to enable inference tasks like prediction, explanation, and planning. The document outlines decomposing action theories into modules to avoid unwanted conclusions and exploit logical modularity when evaluating and revising theories.
The SimpleFPS Planning Domain: A PDDL Benchmark for Proactive NPCsStavros Vassos
Stavros Vassos, and Michail Papakonstantinou, In Proceedings of the Non-Player Character AI workshop (NPCAI-2011) of the Artificial Intelligence & Interactive Digital Entertainment (AIIDE-2011) Conference, Stanford CA, USA, 2011.
http://stavros.lostre.org/2011/09/12/simplefps-int4-2011/
Abstract: In this paper we focus on proactive behavior for nonplayer characters (NPCs) in the first-person shooter (FPS) genre of video games based on goal-oriented planning. Some recent approaches for applying realtime planning in commercial video games show that the existing hardware is starting to follow up on the computing resources needed for such techniques to work well. Nonetheless, it is not clear under which conditions real-time efficiency can be guaranteed. In this paper we give a precise specification of SimpleFPS, a STRIPS planning domain expressed in PDDL that captures some basic planning tasks that may be useful in a first-person shooter video game. This is intended to work as a first step towards quantifying the performance of different planning techniques that may be used in real-time to guide the behavior of NPCs. We present a simple tool we developed for generating random planning problem instances in PDDL with user defined properties, and show some preliminary results based on SimpleFPS instances that vary in the size of the domain and two well-known planners from the planning community.
The SimpleFPS Planning Domain: A PDDL Benchmark for Proactive NPCsStavros Vassos
Stavros Vassos, and Michail Papakonstantinou, In Proceedings of the Non-Player Character AI workshop (NPCAI-2011) of the Artificial Intelligence & Interactive Digital Entertainment (AIIDE-2011) Conference, Stanford CA, USA, 2011.
http://stavros.lostre.org/2011/09/12/simplefps-int4-2011/
Abstract: In this paper we focus on proactive behavior for nonplayer characters (NPCs) in the first-person shooter (FPS) genre of video games based on goal-oriented planning. Some recent approaches for applying realtime planning in commercial video games show that the existing hardware is starting to follow up on the computing resources needed for such techniques to work well. Nonetheless, it is not clear under which conditions real-time efficiency can be guaranteed. In this paper we give a precise specification of SimpleFPS, a STRIPS planning domain expressed in PDDL that captures some basic planning tasks that may be useful in a first-person shooter video game. This is intended to work as a first step towards quantifying the performance of different planning techniques that may be used in real-time to guide the behavior of NPCs. We present a simple tool we developed for generating random planning problem instances in PDDL with user defined properties, and show some preliminary results based on SimpleFPS instances that vary in the size of the domain and two well-known planners from the planning community.
This a set of slides explaining the search methods by
Gradient Descent
Simulated Annealing
Hill Climbing
They are still not great, but they are good enough
A brief overview of the emerging AI field of "General Games". This presentation was originally given as part of the Researchers' Digest series at University of Strathclyde on 14th Dec 2009.
Soumith Chintala, Artificial Intelligence Research Engineer, Facebook at MLco...MLconf
Predicting the Future Using Deep Adversarial Networks: Learning With No Labeled Data: Labeling data to solve a certain task can be expensive, slow and does not scale. If unsupervised learning works, then one can have very little labelled data to help a machine solve a particular task. Most traditional unsupervised learning methods such as PCA and K-means clustering do not work well for complicated data distributions, making them useless for a lot of tasks. In this talk, I’ll go over recent advances in a technique for unsupervised learning called Generative Adversarial networks, which can learn to generate very complicated data distributions such as images and videos. These trained adversarial networks are then used to solve new tasks with very little labeled data, making them an attractive class of algorithms for many domains where there is limited labeled data but unlimited unlabeled data.
This a set of slides explaining the search methods by
Gradient Descent
Simulated Annealing
Hill Climbing
They are still not great, but they are good enough
A brief overview of the emerging AI field of "General Games". This presentation was originally given as part of the Researchers' Digest series at University of Strathclyde on 14th Dec 2009.
Soumith Chintala, Artificial Intelligence Research Engineer, Facebook at MLco...MLconf
Predicting the Future Using Deep Adversarial Networks: Learning With No Labeled Data: Labeling data to solve a certain task can be expensive, slow and does not scale. If unsupervised learning works, then one can have very little labelled data to help a machine solve a particular task. Most traditional unsupervised learning methods such as PCA and K-means clustering do not work well for complicated data distributions, making them useless for a lot of tasks. In this talk, I’ll go over recent advances in a technique for unsupervised learning called Generative Adversarial networks, which can learn to generate very complicated data distributions such as images and videos. These trained adversarial networks are then used to solve new tasks with very little labeled data, making them an attractive class of algorithms for many domains where there is limited labeled data but unlimited unlabeled data.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
What Is a Good Domain Description? Evaluating & Revising Action Theories in Dynamic Logic
1. Introduction
Main Results
Concluding Remarks
What is a Good Domain Description?
Evaluating & Revising Action Theories in Dynamic Logic
Ivan José Varzinczak
IRIT – Université Paul Sabatier
October 27th 2006
Ivan José Varzinczak What is a Good Domain Description?
2. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Problem: describing domains by logical formulas
Actions and their effects
Executabilities of actions
Inexecutabilities of actions
Domain constraints
Example
A turkey that walks is alive
Teasing a turkey makes it walk
It is always possible to tease a turkey
A dead turkey remains dead after teasing it
Ivan José Varzinczak What is a Good Domain Description?
3. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Problem: describing domains by logical formulas
Actions and their effects
Executabilities of actions
Inexecutabilities of actions
Domain constraints
Example
A turkey that walks is alive
Teasing a turkey makes it walk
It is always possible to tease a turkey
A dead turkey remains dead after teasing it
Ivan José Varzinczak What is a Good Domain Description?
4. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Goal: inference tasks
Prediction
Explanation
Planning
Ivan José Varzinczak What is a Good Domain Description?
5. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Prediction: reasoning about the future
?
Initial actions Result
state state
After shooting, the turkey stops walking
Ivan José Varzinczak What is a Good Domain Description?
6. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Explanation: reasoning about the past
?
Initial actions Current
state state
After shooting, the turkey is dead: the gun was loaded
Ivan José Varzinczak What is a Good Domain Description?
7. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Planning: what to do to achieve a goal
?
Current actions Desired
state state
To have the turkey dead: load the gun, then shoot
Ivan José Varzinczak What is a Good Domain Description?
8. Introduction
Main Results
Concluding Remarks
Reasoning About Actions
Other important tasks
Consistency check
Test of executability/inexecutability
Theory change
...
Ivan José Varzinczak What is a Good Domain Description?
9. Introduction
Main Results
Concluding Remarks
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
10. Introduction
Main Results
Concluding Remarks
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
11. Introduction
Main Results
Concluding Remarks
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
12. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
14. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Formalizing Domains
In this work. . .
we have chosen Modal Logic
Weak version of Propositional Dynamic Logic (PDL)
Simple and decidable
With a tableaux-based theorem prover: Lotrec
Ivan José Varzinczak What is a Good Domain Description?
15. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Logical Preliminaries
Ontology
Actions: ) 0($
' % # a 1 a2
1 53331
4 2 2 2
Atomic propositions: ) 0BA386
' @ 9 7 p 1 p2 1 53331
4 2 2 2
Literals: RP$H38GFEC
Q ) I @ 9 7 6 ' % D p p S H38UT
@ 9 7 6 4
Classical formulas: Rba`XV
c ) ' Y W 1 c 1 2 53331
4 2 2 2
Action operators
For each a , a modal operator a
$dT
% # e f
ea : “after execution of a, is true”
c gf c
a : “a is inexecutable”
h
p qi
a a
r
' ts
c def e uQ vgf
c Q
a : “a is executable”
w
y €x
Complex formulas: 1 1 2 3331
2 2 2
Ivan José Varzinczak What is a Good Domain Description?
16. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Logical Preliminaries
Ontology
Actions: ) 0($
' % # a 1 a2
1 53331
4 2 2 2
Atomic propositions: ) 0BA386
' @ 9 7 p 1 p2 1 53331
4 2 2 2
Literals: RP$H38GFEC
Q ) I @ 9 7 6 ' % D p p S H38UT
@ 9 7 6 4
Classical formulas: Rba`XV
c ) ' Y W 1 c 1 2 53331
4 2 2 2
Action operators
For each a , a modal operator a
$dT
% # e f
ea : “after execution of a, is true”
c gf c
a : “a is inexecutable”
h
p qi
a a
r
' ts
c def e uQ vgf
c Q
a : “a is executable”
w
y €x
Complex formulas: 1 1 2 3331
2 2 2
Ivan José Varzinczak What is a Good Domain Description?
17. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Logical Preliminaries
Ontology
Actions: ) 0($
' % # a 1 a2
1 53331
4 2 2 2
Atomic propositions: ) 0BA386
' @ 9 7 p 1 p2 1 53331
4 2 2 2
Literals: RP$H38GFEC
Q ) I @ 9 7 6 ' % D p p S H38UT
@ 9 7 6 4
Classical formulas: Rba`XV
c ) ' Y W 1 c 1 2 53331
4 2 2 2
Action operators
For each a , a modal operator a
$dT
% # e f
ea : “after execution of a, is true”
c gf c
a : “a is inexecutable”
h
p qi
a a
r
' ts
c def e uQ vgf
c Q
a : “a is executable”
w
y €x
Complex formulas: 1 1 2 3331
2 2 2
Ivan José Varzinczak What is a Good Domain Description?
18. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Logical Preliminaries
Ontology
Actions: ) 0($
' % # a 1 a2
1 53331
4 2 2 2
Atomic propositions: ) 0BA386
' @ 9 7 p 1 p2 1 53331
4 2 2 2
Literals: RP$H38GFEC
Q ) I @ 9 7 6 ' % D p p S H38UT
@ 9 7 6 4
Classical formulas: Rba`XV
c ) ' Y W 1 c 1 2 53331
4 2 2 2
Action operators
For each a , a modal operator a
$dT
% # e f
ea : “after execution of a, is true”
c gf c
a : “a is inexecutable”
h
p qi
a a
r
' ts
c def e uQ vgf
c Q
a : “a is executable”
w
y €x
Complex formulas: 1 1 2 3331
2 2 2
Ivan José Varzinczak What is a Good Domain Description?
19. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Logical Preliminaries
Example
Actions: shoot, tease
Propositions: loaded, alive, walking
Formulas: alive walking, tease ,
r
Q ƒ‚ „ s
loaded e shoot alive
†… Q gf
Ivan José Varzinczak What is a Good Domain Description?
20. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Semantics
Multimodal logic K [Popkorn 94,Blackburn et al. 2001].
Definition
Models W R where
r
‡ ' 1 s
W ˆ 2 g‘‰
“ ’ : set of possible worlds (states)
R: –•$
… ” % # 2W — W
Definition
˜
p (p is true at world w of model ) iff p w
w
™ A' ˜ ‡ ˜ T
a iff for every w such that wRa w ,
w w
™ A' e df e e ™ A'
f
the usual truth conditions for the other connectives
Ivan José Varzinczak What is a Good Domain Description?
21. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Semantics
Multimodal logic K [Popkorn 94,Blackburn et al. 2001].
Definition
Models W R where
r
‡ ' 1 s
W ˆ 2 g‘‰
“ ’ : set of possible worlds (states)
R: –•$
… ” % # 2W — W
Definition
˜
p (p is true at world w of model ) iff p w
w
™ A' ˜ ‡ ˜ T
a iff for every w such that wRa w ,
w w
™ A' e df e e ™ A'
f
the usual truth conditions for the other connectives
Ivan José Varzinczak What is a Good Domain Description?
22. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Semantics
Example
If a1 a2 , and p 1 p2 , W R , where
r
) b($
' % # 1 4 ) 0gH386
' @ 9 7 1 4 ‡ ' 1 s
W hb'
) ) p 1 p2
1 ) i4
1 p1 Q 1 p2 Q R) i4
1 p1 p2 1 ih4
1 4
p1 p2 p 1 p2 p 1 p2 p 1 p2
l
R a1
) mj 1 ) i4
1 Q 1 ) mj nR4
1 k 1 Q R) i4
1 1 nR4
1 k
p1 p2 p1 p2 p 1 p2 p1 p2
j k '
Rmj
Q ) 1 Q R) i4
1 1 Rmj nR4
Q ) 1 k 1 ) i4
1 Q 1 o tR4
k
R a2
j k mi0'
) j ) p1 p2
1 ) i4
1 p1 Q 1 p2 ) mj nR4
1 k p1 Q 1 p2 ) i4
1 p1 Q 1 p2 pR4
4 k
is a model
Ivan José Varzinczak What is a Good Domain Description?
23. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Semantics
Example
a1
a1
p1 p2 p1 p2
p1 p2
q r s w A uv
a2
x
p1 a2 p2
w uv
‡ : a1 a1 z {y } g|
p1 a1
w A uv
} ~ {y €
p1 p2 p2 a1
w uv
} z †y ‚|
r ts
a2
Ivan José Varzinczak What is a Good Domain Description?
24. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Semantics
Definition ˜ ˜
iff for all w W,
w
™ '˜ ˜ T ™ '
ƒ „A'
™ iff …A'
™ for every ƒ P†
T
Definition
is a consequence of the set of global axioms in all ˜
ƒ
˜
PDL-models (noted
˜
PDL
) iff for every , if , then
ƒ ' ‡ ™ ' ƒ
™ ' .
Ivan José Varzinczak What is a Good Domain Description?
25. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Semantics
Definition ˜ ˜
iff for all w W,
w
™ '˜ ˜ T ™ '
ƒ „A'
™ iff …A'
™ for every ƒ P†
T
Definition
is a consequence of the set of global axioms in all ˜
ƒ
˜
PDL-models (noted
˜
PDL
) iff for every , if , then
ƒ ' ‡ ™ ' ƒ
™ ' .
Ivan José Varzinczak What is a Good Domain Description?
26. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
27. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
A turkey that walks is alive: walking … alive
Teasing a turkey makes it to walk: e tease walking
f
It is always possible to tease a turkey: tease
r
„ s
A dead turkey remains dead after teasing it
alive tease alive
h
¨ ¦ ¨ Fi
If the gun is loaded, shooting kills the turkey
loaded shoot alive
h
¦ ¨ di
Teasing does not unload the gun
loaded tease loaded
h
¦ i
Ivan José Varzinczak What is a Good Domain Description?
28. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
‡ˆ
ˆ
ˆ
ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
tease
r ‹ ˜
tease alive
„ s 1
ˆ
ˆ
ˆ walking alive … 1
ˆ
ˆ
ˆ
'˜ e f
tease walking alive tease alive alive
ˆ ‰ ˆ
Œˆ
ˆ
ˆ e f ˆ
1 Q Ž' ˜ e †… j €f Q ƒ‚
loaded shoot alive
k
alive tease
e {… Q gf 1
alive tease alive
Q Ž' ˜ e {… ‚f
Q e †… Q gf
alive
1
loaded tease loaded
Š
e †… f '
N.B.: Such a description is consistent
What is the problem?
Ivan José Varzinczak What is a Good Domain Description?
29. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
‡ˆ
ˆ
ˆ
ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
tease
r ‹ ˜
tease alive
„ s 1
ˆ
ˆ
ˆ walking alive … 1
ˆ
ˆ
ˆ
'˜ e f
tease walking alive tease alive alive
ˆ ‰ ˆ
Œˆ
ˆ
ˆ e f ˆ
1 Q Ž' ˜ e †… j €f Q ƒ‚
loaded shoot alive
k
alive tease
e {… Q gf 1
alive tease alive
Q Ž' ˜ e {… ‚f
Q e †… Q gf
alive
1
loaded tease loaded
Š
e †… f '
N.B.: Such a description is consistent
What is the problem?
Ivan José Varzinczak What is a Good Domain Description?
30. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
‡ˆ
ˆ
ˆ
ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
tease
r ‹ ˜
tease alive
„ s 1
ˆ
ˆ
ˆ walking alive … 1
ˆ
ˆ
ˆ
'˜ e f
tease walking alive tease alive alive
ˆ ‰ ˆ
Œˆ
ˆ
ˆ e f ˆ
1 '˜ Q e †… j €f Q ƒ‚
loaded shoot alive
k
alive tease
e {… Q gf 1
alive tease alive
Q Ž' ˜ e {… ‚f
Q e †… Q gf
alive
1
loaded tease loaded
Š
e †… f '
N.B.: Such a description is consistent
What is the problem?
Ivan José Varzinczak What is a Good Domain Description?
31. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
‡ˆ
ˆ
ˆ
ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
tease
r ‹ ˜
tease alive
„ s 1
ˆ
ˆ
ˆ walking alive … 1
ˆ
ˆ
ˆ
e ' ˜ f
tease walking alive tease alive alive
ˆ ‰ ˆ
Œˆ
ˆ
ˆ e f ˆ
1 Q Ž' ˜ e †… j €f Q ƒ‚
loaded shoot alive
k
alive tease
e {… Q gf 1
alive tease alive
Q Ž' ˜ e {… ‚f
Q e †… Q gf
alive
1
loaded tease loaded
Š
e †… f '
N.B.: Such a description is consistent
What is the problem?
Ivan José Varzinczak What is a Good Domain Description?
32. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
‡ˆ
ˆ
ˆ
ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
tease
r ‹ ˜
tease alive
„ s 1
ˆ
ˆ
ˆ walking alive … 1
ˆ
ˆ
ˆ
e ' ˜ f
tease walking alive tease alive alive
ˆ ‰ ˆ
Œˆ
ˆ
ˆ e f ˆ
1 Q Ž' ˜ e †… j €f Q ƒ‚
loaded shoot alive
k
alive tease
e {… Q gf 1
alive tease alive
Q Ž' ˜ e {… ‚f
Q e †… Q gf
alive
1
loaded tease loaded
Š
e †… f '
N.B.: Such a description is consistent
What is the problem?
Ivan José Varzinczak What is a Good Domain Description?
33. Introduction
Describing Action Theories
Main Results
Unwanted Conclusions
Concluding Remarks
The Tale Again
Example
‡ˆ
ˆ
ˆ
ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
ˆ ˆ
tease
r ‹ ˜
tease alive
„ s 1
ˆ
ˆ
ˆ walking alive … 1
ˆ
ˆ
ˆ
e ' ˜ f
tease walking alive tease alive alive
ˆ ‰ ˆ
Œˆ
ˆ
ˆ e f ˆ
1 Q Ž' ˜ e †… j €f Q ƒ‚
loaded shoot alive
k
alive tease
e {… Q gf 1
alive tease alive
Q Ž' ˜ e {… ‚f
Q e †… Q gf
alive
1
loaded tease loaded
Š
e †… f '
N.B.: Such a description is consistent
What is the problem?
Ivan José Varzinczak What is a Good Domain Description?
34. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
35. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Types of domain laws
Static laws : walking … alive
Effect laws : loaded e †… shoot alive
Q ‚f
Executability laws : hasGun shoot
r
… „ s
Inexecutability laws : Q hasGun e †… shoot gf
! only formulas of these types
Ivan José Varzinczak What is a Good Domain Description?
36. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Types of domain laws
Static laws : walking … alive
Effect laws : loaded e †… shoot alive
Q ‚f
Executability laws : hasGun shoot
r
… „ s
Inexecutability laws : Q hasGun e †… shoot gf
! only formulas of these types
Ivan José Varzinczak What is a Good Domain Description?
37. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Types of domain laws
Static laws : walking … alive
Effect laws : loaded e †… shoot alive
Q ‚f
Executability laws : hasGun shoot
r
… „ s
Inexecutability laws : Q hasGun e †… shoot gf
! only formulas of these types
Ivan José Varzinczak What is a Good Domain Description?
38. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Types of domain laws
Static laws : walking … alive
Effect laws : loaded e †… shoot alive
Q ‚f
Executability laws : hasGun shoot
r
… „ s
Inexecutability laws : Q hasGun e †… shoot gf
! only formulas of these types
Ivan José Varzinczak What is a Good Domain Description?
39. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Types of domain laws
Static laws : walking … alive
Effect laws : loaded e †… shoot alive
Q ‚f
Executability laws : hasGun shoot
r
… „ s
Inexecutability laws : Q hasGun e †… shoot gf
! only formulas of these types
Ivan José Varzinczak What is a Good Domain Description?
40. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Defining modules
: set of static laws
‘
Given a $dT
% #
a
: effect laws for a
’
a
: executability laws for a
“
a
: inexecutability laws for a
”
‘ r
a a a : domain description for a
• 1 – n1 — ‚1 s
a, a, and a
• ˜ Ž'
a • 5R™
œ › š – ˜ '
a – 5R™
œ › š — ˜ Ž'
a — 5ž™
œ › š
: the action theory of a given domain
‘ r
‚ nŸ1
— 1 – 1 • s
Ivan José Varzinczak What is a Good Domain Description?
41. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Defining modules
: set of static laws
‘
Given a $dT
% #
a
: effect laws for a
’
a
: executability laws for a
“
a
: inexecutability laws for a
”
‘ r
a a a : domain description for a
• 1 – n1 — ‚1 s
a, a, and a
• ˜ Ž'
a • 5R™
œ › š – ˜ '
a – 5R™
œ › š — ˜ Ž'
a — 5ž™
œ › š
: the action theory of a given domain
‘ r
‚ nŸ1
— 1 – 1 • s
Ivan José Varzinczak What is a Good Domain Description?
42. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Defining modules
: set of static laws
‘
Given a $dT
% #
a
: effect laws for a
’
a
: executability laws for a
“
a
: inexecutability laws for a
”
‘ r
a a a : domain description for a
• 1 – n1 — ‚1 s
a, a, and a
• ˜ Ž'
a • 5R™
œ › š – ˜ '
a – 5R™
œ › š — ˜ Ž'
a — 5ž™
œ › š
: the action theory of a given domain
‘ r
‚ nŸ1
— 1 – 1 • s
Ivan José Varzinczak What is a Good Domain Description?
43. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Natural Modules in Action Theories
Defining modules
: set of static laws
‘
Given a $dT
% #
a
: effect laws for a
’
a
: executability laws for a
“
a
: inexecutability laws for a
”
‘ r
a a a : domain description for a
• 1 – n1 — ‚1 s
a, a, and a
• ˜ Ž'
a • 5R™
œ › š – ˜ '
a – 5R™
œ › š — ˜ Ž'
a — 5ž™
œ › š
: the action theory of a given domain
‘ r
‚ nŸ1
— 1 – 1 • s
Ivan José Varzinczak What is a Good Domain Description?
48. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
What About the Frame Problem?
Restriction on models
For all wRa w :
˜
e
˜
w
p implies p, if a p
w
™ '¬ ˜ ™ '¬ ˜ ¬¥
f
p implies p, if a p.
w w
™ A' ™ H' Q ¥
¬
f
New logical consequence
˜ ˜
® ' instead of '
PDL
Example ˜
loaded wait loaded
‘
‚ nŸ1
— 1 – 1 • ® ' e ¯… f
Ivan José Varzinczak What is a Good Domain Description?
49. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
What About the Frame Problem?
Restriction on models
For all wRa w :
˜
e
˜
w
p implies p, if a p
w
™ '¬ ˜ ™ '¬ ˜ ¬¥
f
p implies p, if a p.
w w
™ A' ™ H' Q ¥
¬
f
New logical consequence
˜ ˜
® ' instead of '
PDL
Example ˜
loaded wait loaded
‘
‚ nŸ1
— 1 – 1 • ® ' e ¯… f
Ivan José Varzinczak What is a Good Domain Description?
50. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
What About the Frame Problem?
Restriction on models
For all wRa w :
˜
e
˜
w
p implies p, if a p
w
™ '¬ ˜ ™ '¬ ˜ ¬¥
f
p implies p, if a p.
w w
™ A' ™ H' Q ¥
¬
f
New logical consequence
˜ ˜
® ' instead of '
PDL
Example ˜
loaded wait loaded
‘
‚ nŸ1
— 1 – 1 • ® ' e ¯… f
Ivan José Varzinczak What is a Good Domain Description?
51. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
What About the Frame Problem?
The dependence-based approach. . .
solves the frame problem
subsumes Reiter’s regression [Demolombe et al. 2003]
does not entirely solve the ramification problem
e.g. shoot ¨ ±° walking
But is the only approach that works for domains with
actions with both indeterminate and indirect effects
[Castilho et al. 2002], [Herzig Varzinczak 2004]
Ivan José Varzinczak What is a Good Domain Description?
52. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
What About the Frame Problem?
The dependence-based approach. . .
solves the frame problem
subsumes Reiter’s regression [Demolombe et al. 2003]
does not entirely solve the ramification problem
e.g. shoot ¨ ±° walking
But is the only approach that works for domains with
actions with both indeterminate and indirect effects
[Castilho et al. 2002], [Herzig Varzinczak 2004]
Ivan José Varzinczak What is a Good Domain Description?
53. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
What About the Frame Problem?
The dependence-based approach. . .
solves the frame problem
subsumes Reiter’s regression [Demolombe et al. 2003]
does not entirely solve the ramification problem
e.g. shoot ¨ ±° walking
But is the only approach that works for domains with
actions with both indeterminate and indirect effects
[Castilho et al. 2002], [Herzig Varzinczak 2004]
Ivan José Varzinczak What is a Good Domain Description?
54. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Outline
1 Introduction
Describing Action Theories
Unwanted Conclusions
2 Main Results
Decomposing Theories
Logical Modularity
Exploiting Modularity
Theory Change
3 Concluding Remarks
Ivan José Varzinczak What is a Good Domain Description?
55. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Consistency and More
Postulates ˜
PC (Consistency):
‘
a a a ˜ ˜
• 1 – n1 — ‚1 ® '¬
PS (No implicit static laws): if
‘
a a a , then
‘
• 1 – n1 — ‚1 ® ' c c Ž'
PI (No implicit inexecutability laws):
˜
if
‘
a a a a
˜
,
• 1 – n1 — ‚1 ® ' e {²c
… ‚f
then a ‘
a
PDL — ‚1 ' e †²c
… gf
PX (No implicit executability laws):
˜
if a a a a ,
‘ ˜ r
• 1 – n1 — ‚1 … ²³'
c ® „ s
then a a
‘ r
PDL – n1 ' … tc „ s
Motivation
Better control what is going on
Ivan José Varzinczak What is a Good Domain Description?
56. Decomposing Theories
Introduction
Logical Modularity
Main Results
Exploiting Modularity
Concluding Remarks
Theory Change
Consistency and More
Postulates ˜
PC (Consistency):
‘
a a a ˜ ˜
• 1 – n1 — ‚1 ® '¬
PS (No implicit static laws): if
‘
a a a , then
‘
• 1 – n1 — ‚1 ® ' c c Ž'
PI (No implicit inexecutability laws):
˜
if
‘
a a a a
˜
,
• 1 – n1 — ‚1 ® ' e {²c
… ‚f
then a ‘
a
PDL — ‚1 ' e †²c
… gf
PX (No implicit executability laws):
˜
if a a a a ,
‘ ˜ r
• 1 – n1 — ‚1 … ²³'
c ® „ s
then a a
‘ r
PDL – n1 ' … tc „ s
Motivation
Better control what is going on
Ivan José Varzinczak What is a Good Domain Description?