This document discusses implementing explanation-based argumentation using answer set programming. It begins with background on argumentation frameworks and their limitations in modeling real arguments. It then presents Pollock's puzzle as an example where current frameworks struggle. The document proposes an approach called explanation-based argumentation that models arguments as explanations for observations. These explanations can be generated, deleted if impossible, and evaluated based on how well they fit observations. The document concludes that answer set programming is well-suited to implement this approach due to its declarative problem modeling and search capabilities.
Bridging Representation of Laws, of Implementations and of BehavioursGiovanni Sileno
Presentation at JURIX 2015 (Legal Knowledge and Information Systems) conference.
To align representations of law, of implementations of law and of concrete behaviours, we designed a common ground representational model for the three domains, based on the notion of position, building upon Petri nets. This paper reports on work to define subsumption between positional models.
On the Interactional Meaning of Fundamental Legal ConceptsGiovanni Sileno
Presentation at JURIX 2014.
Abstract: Rather than as abstract entities, jural relations are analyzed in terms of the bindings they create on the individual behaviour of concurrent social agents. Investigating a simple sale transaction modeled with Petri Nets, we argue that the concepts on the two Hohfeldian squares rely on the implicit reference to a “transcendental” collective entity, to which the two parties believe or are believed to belong. From this perspective, we observe that both liabilities and duties are associated to obligations, respectively of an epistemic or practical nature. The fundamental legal concepts defined by Hohfeld are revisited accordingly, leading to the construction of two Hohfeldian prisms.
The impact of mobile technologies on healthcare is particularly evident in the case of self-management of chronic diseases, where they can decrease spending and improve the patient quality of life. In this talk we propose the adoption of agent-based modelling and simulation techniques as built-in tools to dynamically monitor patient health state and provide feedbacks for self-management. To demonstrate the feasibility of our proposal we focus on Type 1 Diabetes Mellitus as our case study, and provide some preliminary simulation results.
Game Engines to Model MAS: A Research RoadmapAndrea Omicini
Game engines are gaining increasing popularity in various computational research areas, and in particular in the context of Multi-Agent Systems (MAS)—for instance, to render augmented reality environments, improve immersive simulation infrastructures, and so on. Existing examples of successful integration between game engines and MAS still focus on specific technology-level goals, rather than on shaping a general-purpose game-based agent-oriented infrastructure. In this roadmap talk, we point out the conceptual issues to be faced to exploit game engines as agent-oriented infrastructures, and outline a possible research roadmap to follow, backed up by some early experiments involving the Unity3D engine.
Privacy through Anonymisation in Large-scale Socio-technical Systems: The BIS...Andrea Omicini
Large-scale socio-technical systems (STS) inextricably inter-connect individual – e.g., the right to privacy –, social – e.g., the effectiveness of organisational processes –, and technology issues —e.g., the software engineering process. As a result, the design of the complex software infrastructure involves also non-technological aspects such as the legal ones—so that, e.g., law-abidingness can be ensured since the early stages of the software engineering process. By focussing on contact centres (CC) as relevant examples of knowledge-intensive STS, we elaborate on the articulate aspects of anonymisation: there, individual and organisational needs clash, so that only an accurate balancing between legal and technical aspects could possibly ensure the system efficiency while preserving the individual right to privacy. We discuss first the overall legal framework, then the general theme of anonymisation in CC. Finally we overview the technical process developed in the context of the BISON project.
Project presentation @ DMI, Università di Catania, Italy, 25 July 2016
Bridging Representation of Laws, of Implementations and of BehavioursGiovanni Sileno
Presentation at JURIX 2015 (Legal Knowledge and Information Systems) conference.
To align representations of law, of implementations of law and of concrete behaviours, we designed a common ground representational model for the three domains, based on the notion of position, building upon Petri nets. This paper reports on work to define subsumption between positional models.
On the Interactional Meaning of Fundamental Legal ConceptsGiovanni Sileno
Presentation at JURIX 2014.
Abstract: Rather than as abstract entities, jural relations are analyzed in terms of the bindings they create on the individual behaviour of concurrent social agents. Investigating a simple sale transaction modeled with Petri Nets, we argue that the concepts on the two Hohfeldian squares rely on the implicit reference to a “transcendental” collective entity, to which the two parties believe or are believed to belong. From this perspective, we observe that both liabilities and duties are associated to obligations, respectively of an epistemic or practical nature. The fundamental legal concepts defined by Hohfeld are revisited accordingly, leading to the construction of two Hohfeldian prisms.
The impact of mobile technologies on healthcare is particularly evident in the case of self-management of chronic diseases, where they can decrease spending and improve the patient quality of life. In this talk we propose the adoption of agent-based modelling and simulation techniques as built-in tools to dynamically monitor patient health state and provide feedbacks for self-management. To demonstrate the feasibility of our proposal we focus on Type 1 Diabetes Mellitus as our case study, and provide some preliminary simulation results.
Game Engines to Model MAS: A Research RoadmapAndrea Omicini
Game engines are gaining increasing popularity in various computational research areas, and in particular in the context of Multi-Agent Systems (MAS)—for instance, to render augmented reality environments, improve immersive simulation infrastructures, and so on. Existing examples of successful integration between game engines and MAS still focus on specific technology-level goals, rather than on shaping a general-purpose game-based agent-oriented infrastructure. In this roadmap talk, we point out the conceptual issues to be faced to exploit game engines as agent-oriented infrastructures, and outline a possible research roadmap to follow, backed up by some early experiments involving the Unity3D engine.
Privacy through Anonymisation in Large-scale Socio-technical Systems: The BIS...Andrea Omicini
Large-scale socio-technical systems (STS) inextricably inter-connect individual – e.g., the right to privacy –, social – e.g., the effectiveness of organisational processes –, and technology issues —e.g., the software engineering process. As a result, the design of the complex software infrastructure involves also non-technological aspects such as the legal ones—so that, e.g., law-abidingness can be ensured since the early stages of the software engineering process. By focussing on contact centres (CC) as relevant examples of knowledge-intensive STS, we elaborate on the articulate aspects of anonymisation: there, individual and organisational needs clash, so that only an accurate balancing between legal and technical aspects could possibly ensure the system efficiency while preserving the individual right to privacy. We discuss first the overall legal framework, then the general theme of anonymisation in CC. Finally we overview the technical process developed in the context of the BISON project.
Project presentation @ DMI, Università di Catania, Italy, 25 July 2016
The following text material and terms defined at the end comprise .docxarnoldmeredith47041
The following text material and terms defined at the end comprise part of what will be asked on the Mid-Term Exam for PHIL 1381.
Logic [excerpt from Stan Baronett, Logic, 2E]
Logic is the study of reasoning. Logic investigates the level of correctness of the reasoning found in arguments. An argument is a group of statements of which one (the conclusion) is claimed to follow from the others (the premises). A statement is a sentence that is either true or false. Every statement is either true or false; these two possibilities are called “truth values.” Premises are statements that contain information intended to provide support or reasons to believe a conclusion. The conclusion is the statement that is claimed to follow from the premises. In order to help recognize arguments, we rely on premise indicator words and phrases, and conclusion indicator words and phrases.
Inference is the term used by logicians to refer to the reasoning process that is expressed by an argument. If a passage expresses a reasoning process—that the conclusion follows from the premises—then we say that it makes an inferential claim. If a passage does not express a reasoning process (explicit or implicit), then it does not make an inferential claim (it is a noninferential passage). One type of noninferential passage is the explanation. An explanation provides reasons for why or how an event occurred. By themselves, explanations are not arguments; however, they can form part of an argument.
There are two types of argument: deductive and inductive. A deductive argument is one in which it is claimed that the conclusion follows necessarily from the premises. In other words, it is claimed that under the assumption that the premises are true it is impossible for the conclusion to be false. An inductive argument is one in which it is claimed that the premises make the conclusion probable. In other words, it is claimed that, under the assumption that the premises are true, it is improbable for the conclusion to be false.
Revealing the logical form of a deductive argument helps with logical analysis and evaluation. When we evaluate deductive arguments, we use the following concepts: valid, invalid, sound, and unsound. A valid argument is one where, assuming the premises are true, it is impossible for the conclusion to be false. In other words, the conclusion follows necessarily from the premises. An invalid argument is one where, assuming the premises are true, it is possible for the conclusion to be false. In other words, a deductive argument in which the conclusion does not follow necessarily from the premises is an invalid argument. When logical analysis shows that a deductive argument is valid, and when truth value analysis of the premises shows that they are all true, then the argument is sound. If a deductive argument is invalid, or if at least one of the premises is false (truth value analysis), then the argument is unsound.
A counterexample to astatement is evidenc.
The Evaluation ArgumentChapter 14, Practical ArgumentMig.docxtodd701
The Evaluation Argument
Chapter 14, Practical Argument
Mignette Dorsey
The Evaluation Argument
• Evaluate – To make a personal, value judgment about something
or someone. Ex. A product, service, program, work of literature,
etc.
• Do we evaluate options before we make decisions? Examples?
• Evaluation Argument (options)
1. Make a positive or negative judgment
2. Assert that someone else’s positive or negative judgment is
inaccurate
3. Comparative analysis where you prove one thing is superior to
another
The Evaluation Argument
• What makes another perceive that your evaluation is fair?
• Addressing the Opposing Point of View
• Evidence of bias (p. 477) – Bias can be detected by tone as evidenced
by word choice
• Criteria for Evaluation:
1. Answer the “why” question related to your assertion: Why are
afternoon classes better than morning classes - or vice versa?
2. Establish a list of criteria you will examine: Alertness, Instructor
accessibility, traffic
The Evaluation Argument
3. Comparing criteria - discuss drawbacks of morning classes versus
advantages of afternoon classes
• See pg. 479 re. Evaluation Argument structure
• Evaluation Grammar: Comparatives and Superlatives – page 484
Pop Quiz: Evaluation Essay
(Answers are on the last slide)
Write true or false for each item below:
1. Offering a solution to the problem of student plagiarism would be a
good topic for an evaluation essay.
2. Word choice is an important consideration in writing an evaluation
essay.
3. Word choice establishes the tone of an essay.
4. Of the five “Ws,” the “why” question is never the focus of an
evaluation essay.
5. When we “evaluate,” we make a value judgment about something
or someone.
Pop Quiz Answers: Evaluation Essay
• 1. False
• 2. True
• 3. True
• 4. False
• 5. True
The Evaluation Argument�Chapter 14, Practical ArgumentThe Evaluation Argument�The Evaluation ArgumentThe Evaluation ArgumentPop Quiz: Evaluation Essay�(Answers are on the last slide)Pop Quiz Answers: Evaluation Essay
Rhetoric, Persuasion,
Argumentation:
The Argumentative Essay
Mignette Dorsey
Engl 1302
What is Rhetoric?
• Rhetoric is the ancient art of
argumentation and discourse. When we
write or speak to convince others of what
we believe, we are "rhetors." When we
analyze the way rhetoric works, we are
"rhetoricians." The earliest known studies
of rhetoric come from the Golden Age,
when philosophers of ancient Greece
discussed logos, ethos, and pathos.
Carson-Newman University
https://web.cn.edu/.../resource rhet.html
https://web.cn.edu/kwheeler/logic.html
https://web.cn.edu/kwheeler/ethos.html
https://web.cn.edu/kwheeler/pathos.html
What is Rhetoric?
• Rhetoric / Persuasion is not only written
discourse. Marketing experts use rhetoric for the
purpose of persuading audiences to pay
attention to what they are selling. See
Google>advertisements>images
• Architects use rhetoric in building design.
Consider the message co.
The Evaluation ArgumentChapter 14, Practical ArgumentMig.docxarnoldmeredith47041
The Evaluation Argument
Chapter 14, Practical Argument
Mignette Dorsey
The Evaluation Argument
• Evaluate – To make a personal, value judgment about something
or someone. Ex. A product, service, program, work of literature,
etc.
• Do we evaluate options before we make decisions? Examples?
• Evaluation Argument (options)
1. Make a positive or negative judgment
2. Assert that someone else’s positive or negative judgment is
inaccurate
3. Comparative analysis where you prove one thing is superior to
another
The Evaluation Argument
• What makes another perceive that your evaluation is fair?
• Addressing the Opposing Point of View
• Evidence of bias (p. 477) – Bias can be detected by tone as evidenced
by word choice
• Criteria for Evaluation:
1. Answer the “why” question related to your assertion: Why are
afternoon classes better than morning classes - or vice versa?
2. Establish a list of criteria you will examine: Alertness, Instructor
accessibility, traffic
The Evaluation Argument
3. Comparing criteria - discuss drawbacks of morning classes versus
advantages of afternoon classes
• See pg. 479 re. Evaluation Argument structure
• Evaluation Grammar: Comparatives and Superlatives – page 484
Pop Quiz: Evaluation Essay
(Answers are on the last slide)
Write true or false for each item below:
1. Offering a solution to the problem of student plagiarism would be a
good topic for an evaluation essay.
2. Word choice is an important consideration in writing an evaluation
essay.
3. Word choice establishes the tone of an essay.
4. Of the five “Ws,” the “why” question is never the focus of an
evaluation essay.
5. When we “evaluate,” we make a value judgment about something
or someone.
Pop Quiz Answers: Evaluation Essay
• 1. False
• 2. True
• 3. True
• 4. False
• 5. True
The Evaluation Argument�Chapter 14, Practical ArgumentThe Evaluation Argument�The Evaluation ArgumentThe Evaluation ArgumentPop Quiz: Evaluation Essay�(Answers are on the last slide)Pop Quiz Answers: Evaluation Essay
Rhetoric, Persuasion,
Argumentation:
The Argumentative Essay
Mignette Dorsey
Engl 1302
What is Rhetoric?
• Rhetoric is the ancient art of
argumentation and discourse. When we
write or speak to convince others of what
we believe, we are "rhetors." When we
analyze the way rhetoric works, we are
"rhetoricians." The earliest known studies
of rhetoric come from the Golden Age,
when philosophers of ancient Greece
discussed logos, ethos, and pathos.
Carson-Newman University
https://web.cn.edu/.../resource rhet.html
https://web.cn.edu/kwheeler/logic.html
https://web.cn.edu/kwheeler/ethos.html
https://web.cn.edu/kwheeler/pathos.html
What is Rhetoric?
• Rhetoric / Persuasion is not only written
discourse. Marketing experts use rhetoric for the
purpose of persuading audiences to pay
attention to what they are selling. See
Google>advertisements>images
• Architects use rhetoric in building design.
Consider the message co.
Presentation given at the CRCL 2022: Computational ‘law’ on edge conference (CRCL2022), Brussels, 3 ovember 2022 https://www.cohubicol.com/about/conference-crcl-2022/
The following text material and terms defined at the end comprise .docxarnoldmeredith47041
The following text material and terms defined at the end comprise part of what will be asked on the Mid-Term Exam for PHIL 1381.
Logic [excerpt from Stan Baronett, Logic, 2E]
Logic is the study of reasoning. Logic investigates the level of correctness of the reasoning found in arguments. An argument is a group of statements of which one (the conclusion) is claimed to follow from the others (the premises). A statement is a sentence that is either true or false. Every statement is either true or false; these two possibilities are called “truth values.” Premises are statements that contain information intended to provide support or reasons to believe a conclusion. The conclusion is the statement that is claimed to follow from the premises. In order to help recognize arguments, we rely on premise indicator words and phrases, and conclusion indicator words and phrases.
Inference is the term used by logicians to refer to the reasoning process that is expressed by an argument. If a passage expresses a reasoning process—that the conclusion follows from the premises—then we say that it makes an inferential claim. If a passage does not express a reasoning process (explicit or implicit), then it does not make an inferential claim (it is a noninferential passage). One type of noninferential passage is the explanation. An explanation provides reasons for why or how an event occurred. By themselves, explanations are not arguments; however, they can form part of an argument.
There are two types of argument: deductive and inductive. A deductive argument is one in which it is claimed that the conclusion follows necessarily from the premises. In other words, it is claimed that under the assumption that the premises are true it is impossible for the conclusion to be false. An inductive argument is one in which it is claimed that the premises make the conclusion probable. In other words, it is claimed that, under the assumption that the premises are true, it is improbable for the conclusion to be false.
Revealing the logical form of a deductive argument helps with logical analysis and evaluation. When we evaluate deductive arguments, we use the following concepts: valid, invalid, sound, and unsound. A valid argument is one where, assuming the premises are true, it is impossible for the conclusion to be false. In other words, the conclusion follows necessarily from the premises. An invalid argument is one where, assuming the premises are true, it is possible for the conclusion to be false. In other words, a deductive argument in which the conclusion does not follow necessarily from the premises is an invalid argument. When logical analysis shows that a deductive argument is valid, and when truth value analysis of the premises shows that they are all true, then the argument is sound. If a deductive argument is invalid, or if at least one of the premises is false (truth value analysis), then the argument is unsound.
A counterexample to astatement is evidenc.
The Evaluation ArgumentChapter 14, Practical ArgumentMig.docxtodd701
The Evaluation Argument
Chapter 14, Practical Argument
Mignette Dorsey
The Evaluation Argument
• Evaluate – To make a personal, value judgment about something
or someone. Ex. A product, service, program, work of literature,
etc.
• Do we evaluate options before we make decisions? Examples?
• Evaluation Argument (options)
1. Make a positive or negative judgment
2. Assert that someone else’s positive or negative judgment is
inaccurate
3. Comparative analysis where you prove one thing is superior to
another
The Evaluation Argument
• What makes another perceive that your evaluation is fair?
• Addressing the Opposing Point of View
• Evidence of bias (p. 477) – Bias can be detected by tone as evidenced
by word choice
• Criteria for Evaluation:
1. Answer the “why” question related to your assertion: Why are
afternoon classes better than morning classes - or vice versa?
2. Establish a list of criteria you will examine: Alertness, Instructor
accessibility, traffic
The Evaluation Argument
3. Comparing criteria - discuss drawbacks of morning classes versus
advantages of afternoon classes
• See pg. 479 re. Evaluation Argument structure
• Evaluation Grammar: Comparatives and Superlatives – page 484
Pop Quiz: Evaluation Essay
(Answers are on the last slide)
Write true or false for each item below:
1. Offering a solution to the problem of student plagiarism would be a
good topic for an evaluation essay.
2. Word choice is an important consideration in writing an evaluation
essay.
3. Word choice establishes the tone of an essay.
4. Of the five “Ws,” the “why” question is never the focus of an
evaluation essay.
5. When we “evaluate,” we make a value judgment about something
or someone.
Pop Quiz Answers: Evaluation Essay
• 1. False
• 2. True
• 3. True
• 4. False
• 5. True
The Evaluation Argument�Chapter 14, Practical ArgumentThe Evaluation Argument�The Evaluation ArgumentThe Evaluation ArgumentPop Quiz: Evaluation Essay�(Answers are on the last slide)Pop Quiz Answers: Evaluation Essay
Rhetoric, Persuasion,
Argumentation:
The Argumentative Essay
Mignette Dorsey
Engl 1302
What is Rhetoric?
• Rhetoric is the ancient art of
argumentation and discourse. When we
write or speak to convince others of what
we believe, we are "rhetors." When we
analyze the way rhetoric works, we are
"rhetoricians." The earliest known studies
of rhetoric come from the Golden Age,
when philosophers of ancient Greece
discussed logos, ethos, and pathos.
Carson-Newman University
https://web.cn.edu/.../resource rhet.html
https://web.cn.edu/kwheeler/logic.html
https://web.cn.edu/kwheeler/ethos.html
https://web.cn.edu/kwheeler/pathos.html
What is Rhetoric?
• Rhetoric / Persuasion is not only written
discourse. Marketing experts use rhetoric for the
purpose of persuading audiences to pay
attention to what they are selling. See
Google>advertisements>images
• Architects use rhetoric in building design.
Consider the message co.
The Evaluation ArgumentChapter 14, Practical ArgumentMig.docxarnoldmeredith47041
The Evaluation Argument
Chapter 14, Practical Argument
Mignette Dorsey
The Evaluation Argument
• Evaluate – To make a personal, value judgment about something
or someone. Ex. A product, service, program, work of literature,
etc.
• Do we evaluate options before we make decisions? Examples?
• Evaluation Argument (options)
1. Make a positive or negative judgment
2. Assert that someone else’s positive or negative judgment is
inaccurate
3. Comparative analysis where you prove one thing is superior to
another
The Evaluation Argument
• What makes another perceive that your evaluation is fair?
• Addressing the Opposing Point of View
• Evidence of bias (p. 477) – Bias can be detected by tone as evidenced
by word choice
• Criteria for Evaluation:
1. Answer the “why” question related to your assertion: Why are
afternoon classes better than morning classes - or vice versa?
2. Establish a list of criteria you will examine: Alertness, Instructor
accessibility, traffic
The Evaluation Argument
3. Comparing criteria - discuss drawbacks of morning classes versus
advantages of afternoon classes
• See pg. 479 re. Evaluation Argument structure
• Evaluation Grammar: Comparatives and Superlatives – page 484
Pop Quiz: Evaluation Essay
(Answers are on the last slide)
Write true or false for each item below:
1. Offering a solution to the problem of student plagiarism would be a
good topic for an evaluation essay.
2. Word choice is an important consideration in writing an evaluation
essay.
3. Word choice establishes the tone of an essay.
4. Of the five “Ws,” the “why” question is never the focus of an
evaluation essay.
5. When we “evaluate,” we make a value judgment about something
or someone.
Pop Quiz Answers: Evaluation Essay
• 1. False
• 2. True
• 3. True
• 4. False
• 5. True
The Evaluation Argument�Chapter 14, Practical ArgumentThe Evaluation Argument�The Evaluation ArgumentThe Evaluation ArgumentPop Quiz: Evaluation Essay�(Answers are on the last slide)Pop Quiz Answers: Evaluation Essay
Rhetoric, Persuasion,
Argumentation:
The Argumentative Essay
Mignette Dorsey
Engl 1302
What is Rhetoric?
• Rhetoric is the ancient art of
argumentation and discourse. When we
write or speak to convince others of what
we believe, we are "rhetors." When we
analyze the way rhetoric works, we are
"rhetoricians." The earliest known studies
of rhetoric come from the Golden Age,
when philosophers of ancient Greece
discussed logos, ethos, and pathos.
Carson-Newman University
https://web.cn.edu/.../resource rhet.html
https://web.cn.edu/kwheeler/logic.html
https://web.cn.edu/kwheeler/ethos.html
https://web.cn.edu/kwheeler/pathos.html
What is Rhetoric?
• Rhetoric / Persuasion is not only written
discourse. Marketing experts use rhetoric for the
purpose of persuading audiences to pay
attention to what they are selling. See
Google>advertisements>images
• Architects use rhetoric in building design.
Consider the message co.
Presentation given at the CRCL 2022: Computational ‘law’ on edge conference (CRCL2022), Brussels, 3 ovember 2022 https://www.cohubicol.com/about/conference-crcl-2022/
Presentation given at the 3rd International Workshop on Cognition: Interdisciplinary Foundations, Models and Applications (CIFMA2021), joint with SEFM 2021
Operationalizing Declarative and Procedural KnowledgeGiovanni Sileno
Operationalizing Declarative and Procedural Knowledge: a Benchmark on Logic Programming Petri Nets (LPPNs)
International Conference on Logic Programming (ICPL2020) Workshop on Causal Reasoning and Explanation in Logic Programming (CAUSAL2020)
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
On the problems of interface: explainability, conceptual spaces, relevanceGiovanni Sileno
Summary talk of the research conducted at Télécom ParisTech and Paris Dauphine University during my postdoc project (2016-2018), in collaboration with Isabelle Bloch, Jamal Atif and Jean-Louis Dessalles.
A Petri net-based notation for normative modeling: evaluation on deontic para...Giovanni Sileno
Presentation at MiReL workshop @ ICAIL 2017, the workshop on MIning and REasoning with Legal texts at the International Conference on Artificial Intelligence and Law
Presentation at AICOL Workshop: AI Approaches to the Complexity of Legal Systems
Abstract: the paper is an investigation on how behaviour relates to norms, i.e. on how a certain conduct acquires meaning in institutional terms. The simplest example of this phenomenon is given by the ’count-as’ relation, generally associated to constitutive rules, through which an agent has the legal capacity, via performing a certain action, to create, modify or destroy a certain institutional fact. In the literature, however, the ‘count-as’ relation is mostly accounted for its classificatory functions. Introducing an extension of the Petri Net notation, we argue that the structure of constitutive rules cannot be completely captured by logic conditionals, nor by causal connectives, but it can approached by the notion of supervenience.
Inspired by research in precedential reasoning in Law (amongst others, by Horty), I present a set of algorithms for the conversion of rule base from priority-based and constraint-based representations and viceversa. I explore as well a simple optimization mechanism, using assumptions about the world, providing a model of environmental adaptation
Legal Knowledge Conveyed by Narratives: towards a representational modelGiovanni Sileno
The paper investigates a representational model for narratives, aiming to facilitate the acquisition of the systematic core of stories concerning legal cases, i.e. the set of causal and temporal relationships that govern the world in which the narrated scenario takes place. At the discourse level, we consider narratives as sequences of messages collected in an observation, including descriptions of agents, of agents’ behaviour and of mechanisms relative to physical, mental and institutional domains. At the content level, stories correspond to synchronizations of embodied agent-roles scripts. Following this approach, the Pierson v Post case is analyzed in detail and represented as a Petri net.
Implementing Explanation-Based Argumentation using Answer Set Programming
1. Implementing Explanation-Based
Argumentation using Answer Set
Programming
Giovanni Sileno g.sileno@uva.nl
Alexander Boer, Tom van Engers
Leibniz Center for Law
University of Amsterdam
5 May 2014, ArgMAS presentation
3. Argumentation
● Argumentation is traditionally
seen in terms of attack and
support relationships between
claims brought by participants
in a conversation.
4. Argumentation
● Argumentation is traditionally
seen in terms of attack and
support relationships between
claims brought by participants
in a conversation.
● Argumentation seems to
operate at a meta-level in
respect to the content of
arguments.
6. Formal Argumentation
● An Argumentation framework (AF) [Dung] consists of :
– a set of arguments
– attack relations between arguments
● Formal argumentation frameworks
essentially target this meta-level
7. Formal Argumentation
● To interpret/evaluate an AF we need a semantics.
● For instance, extension-based semantics classify
sub-sets of arguments collectively acceptable in
extensions:
→ the justification state of argument is defined in terms of
memberships to extensions (skeptically/credulously justified)
8. Application of AFs
● Considering the whole process of application of
argumentation theories, we recognize three steps:
– Observation
– Modeling/Reduction to AF
– Analysis of AF
traditional focus of
formal argumentation
observer
modeler
analyst
10. Inside/Outside of Argument Systems
● In general, the extraction of attack relations may be
problematic.
● Trivial case: a claim is explicitly directed against
another claim (syntaxic definition of attack).
11. Inside/Outside of Argument Systems
● In general, the extraction of attack relations may be
problematic.
● In a more general case, however, modelers have to
use some background knowledge and underlying
knowledge processing to identify the attacks.
12. Inside/Outside of Argument Systems
● Usual solution: to integrate in the modeling phase
default/defeasible reasoning.
● e.g. assumption-based argumentation (ABA)
– Argument: conclusion ← assumptions
– Attack to an argument holds if the “contrary” of its
assumptions can be proved, or of its conclusion
(rebuttal).
13. Inside/Outside of Argument Systems
● In practice in ABA the stress is on the support relation,
expressed via defeasible rules, and used to extract the
correspondendent AF.
(Part of) modeling is integrated, but
still concerned by the meta-level!
– Observation
– Modeling/Reduction to AF
– Analysis of AF
observer
modeler
analyst
15. An interesting puzzle by Pollock
● John Pollock presents in in “Reasoning and
probability”, Law, Probability, Risk (2007) a lucid
analysis about the difficulties in reproducing certain
intuitive properties with current formal argumentation
theories.
16. A) Jones says that the gunman
had a moustache.
B) Paul says that Jones was
looking the other way and did
not see what happened.
C) Jacob says that Jones was
watching carefully and had a
clear view of the gunman.
An interesting puzzle by Pollock
17. A) Jones says that the gunman
had a moustache.
B) Paul says that Jones was
looking the other way and did
not see what happened.
C) Jacob says that Jones was
watching carefully and had a
clear view of the gunman.
An interesting puzzle by Pollock
18. A) Jones says that the gunman
had a moustache.
B) Paul says that Jones was
looking the other way and did
not see what happened.
C) Jacob says that Jones was
watching carefully and had a
clear view of the gunman.
An interesting puzzle by Pollock
23. Targeting intuitive properties
1. we should not believe to Jones'
claim (i.e. the zombie argument)
carelessly
2. if we assume Paul more
trustworthy than Jacob, Paul's
claim should be justified but to a
lesser degree
24. Targeting intuitive properties
1. we should not believe to Jones'
claim (i.e. the zombie argument)
carelessly
2. if we assume Paul more
trustworthy than Jacob, Paul's
claim should be justified but to a
lesser degree
3. if Jacob had confirmed Paul's
claim, its degree of justification
should have increased
25. Pollock's puzzle
● Underlying problems:
– zombie arguments
– (relative) judgments of trustworthiness/reliability
– ...
– how to approach justification?
● Pollock proposed a highly elaborate preliminary
solution based on probable probabilities.
● We propose a different solution, based on
explanation-based argumentation.
27. Explanation-Based Argumentation
● Argumentation can be seen as
a dialectical process, in which
parties produce and receive
messages.
● Argumentation does not
concern only the matter of
debate (e.g. a case, or story),
but also the meta-story about
about the construction of such
story.
28. EBA: observations
● The sequence of collected
messages consists in the
observation.
● Sometimes the
observation is collected by
a third-party adjudicator,
entitled to interpret the
case from a neutral
position.The Trial of Bill Burn under
Martin's Act [1838]
29. EBA: explanations
● Given a disputed case, an explanation is a possible
scenario which is compatible
– with the content of the messages, and
– with the generation process of the messages.
In general, the nature of such scenarios is of a multi-
representational model, integrating physical, mental,
institutional and abstract domains.
30. EBA: explanations
● Given a disputed case, an explanation is a possible
scenario which is compatible
– with the content of the messages, and
– with the generation process of the messages.
● An explanation is valid if it reproduces the observation.
● Several explanations may be valid, i.e. fitting the same
observation. Their competition is matter of justification.
31. EBA: space of explanations
conclusion
support
assumptions
explanation
message
confirms
space of
hypothetical
explanations
explanation
message
disconfirms
space of
hypothetical
explanations
argument
argument
attacks
● Instead of being a static entity, the space of
(hypothetical) explanations changes because of
– the incremental nature of the observation (introducing new factors
and constraints),
– changes in strengths of epistemic commitment.
34. Explanation-based Argumentation
1. Generation
– Relevant factors, related to the observation, are grounded into
scenarios
2. Deletion
– Impossible scenarios are removed, leaving a set of hypothetical
explanations
35. Explanation-based Argumentation
1. Generation
– Relevant factors, related to the observation, are grounded into
scenarios
2. Deletion
– Impossible scenarios are removed, leaving a set of hypothetical
explanations
Operational assumption: effective capacity of generating
adequate scenarios
36. Explanation-based Argumentation
1. Generation
– Relevant factors, related to the observation, are grounded into
scenarios
2. Deletion
– Impossible scenarios are removed, leaving a set of hypothetical
explanations
– Hypothetical explanations fitting the observation select the
explanations
37. Explanation-based Argumentation
1. Generation
– Relevant factors, related to the observation, are grounded into
scenarios
2. Deletion
– Impossible scenarios are removed, leaving a set of hypothetical
explanations
– Hypothetical explanations fitting the observation select the
explanations
Informational assumption: an observation either fits an
explanation or it doesn’t.
38. Explanation-based Argumentation
1. Generation
– Relevant factors, related to the observation, are grounded into
scenarios
2. Deletion
– Impossible scenarios are removed, leaving a set of hypothetical
explanations
– Hypothetical explanations fitting the observation select the
explanations
3. Justification
– The relative position of explanations depends on the strengths of
epistemic commitment
39. Explanation-based Argumentation
● Argumentation frameworks based on defeasible
reasoning insist on the inferential aspect of the
problem, rather than the selection of an adequate
search space.
● The selection of (hypothetical) explanations hides
already a certain commitment.
● Hypothetical explanations can be associated to a
certain likelihood (prior).
● After some relevant message, the likelihood, i.e. the
“strength” of explanations should change (posterior).
40. EBA: evaluation of explanations
● Bayesian probability
– Subjective interpretation: probability counts as a
measure of the strength of belief.
– L(E|O) = P(O|E)
41. EBA: evaluation of explanations
● A relative ordinal judgment can be evaluated
calculating the confirmation value for each explanation
E (taken from Tentori, 2007):
42. EBA: evaluation of explanations
● A relative ordinal judgment can be evaluated
calculating the confirmation value for each explanation
E (taken from Tentori, 2007):
Well-known explanatory space assumption:
P(E1
) + P(E2
) + .. + P(En
) ~ 1
44. Implementation of EBA in ASP
● Answer set programming is a declarative programming
paradigm based on the stable-model semantic,
oriented towards difficult (NP-hard) search problems.
– In ASP, similarly to Prolog, the programmer models a problem in
terms of rules and facts, instead of specifying an algorithm. The
resulting code is given as input to a solver, which returns multiple
answer sets or stable models satisfying the problem.
● We take advantage of the search capabilities of ASP
solvers, in order to effectively perform the generation
and deletion steps at once.
45. Implementation of EBA in ASP
● An ASP program related to an explanation-based
argumentation consists of 3 parts:
1. allocation choices, grounding all permutations of
relevant factors,
2. world properties and ground facts, modeling shared
assumptions,
3. observation, modeling the collected messages.
46. Implementation of EBA in ASP
● An ASP program related to an explanation-based
argumentation consists of 3 parts:
1. allocation choices, grounding all permutations of
relevant factors,
2. world properties and ground facts, modeling shared
assumptions,
3. observation, modeling the collected messages.
● The ASP solver gives as output hypothetical
explanations (with 1+2) and explanations (1+2+3).
– Assigning a prior probability to hyp. explanations,
and analysing the fi nal explanations we
calculate the confi rmation values.
47. A) Jones says that the gunman
had a moustache.
B) Paul says that Jones was
looking the other way and did
not see what happened.
C) Jacob says that Jones was
watching carefully and had a
clear view of the gunman.
Relevant factors?
48. ● what an agent says may hold or not
● an agent may be reliable or not
● when he is reliable, what he says is what it holds.
Relevant factors for assertion
49. ● what an agent says may hold or not
● an agent may be reliable or not
● when he is reliable, what he says is what it holds.
● e.g. Paul says Jones was not seeing the gunman.
Writing “Paul is reliable” as paul and “Jones was
seeing” as eye, we have:
1{eye, -eye}1.
1{paul, -paul}1.
-eye :- paul.
Relevant factors for assertion
50. Implementation of the puzzle in ASP
● An ASP program related to an explanation-based
argumentation consists of 3 parts:
1. allocation choices, grounding all permutations of
relevant factors:
1{moustache, -moustache}1.
1{eye, -eye}1.
1{jones, -jones}1.
1{paul, -paul}1.
1{jacob, -jacob}1.
51. Implementation of the puzzle in ASP
● An ASP program related to an explanation-based
argumentation consists of 3 parts:
1. allocation choices,
2. world properties and ground facts, modeling shared
assumptions:
eye :- jones.
52. Implementation of the puzzle in ASP
● An ASP program related to an explanation-based
argumentation consists of 3 parts:
1. allocation choices,
2. world properties and ground facts,
3. observation, modeling the collected messages:
moustache :- jones.
-eye :- paul.
eye :- jacob.
53. Results
● We model the puzzle incrementally, so as to analyze
the impact of each new message.
54. Prior probabilities
● How to calculate the prior probabilities?
● As we know all relevant factors characterizing the
explanations, assuming that they are independent in
the allocation phase we have:
P(Ei
) = P(f1
) * P(f2
) * … * P(fn
)
● A neutral perspective is obtained assuming P(fi
) = 0.5
● As the inclusion of world properties and ground facts
descrease the number of explanations, a normalization
phase is required.
55. Evaluation vs targeted properties
● we should not believe to Jones' claim carelessly
→ explanations in which the gunman has the moustache or not
are confirmed to the same degree
56. Evaluation vs targeted properties
● if we assume Paul more trustworthy than Jacob, Paul's
claim should be justified but to a lesser degree
→ the explanation in which Paul tells the truth
is more confirmed than the others.
57. Evaluation vs targeted properties
● if Jacob had confirmed Paul's claim, its degree of
justification should have increased.
→ explanations where they both say the truth are confirmed as
much as explanations in which they both lie.
58. Extraction of attack/support
● For each observation, we can refer to two dimensions
of change:
– post Oi
− pre Oi
– post Oi
− post Oi-1
59. Conclusion
● With EBA we stress the sharing of a deep-model of the
domain, a model for the observation and the
explicitation of strength of commitments for the
justification.
– (Modeling) the observation
– (Modeling) the deep model
– Extracting (justified)
explanations / AF
observer
modeler
analyst
60. Conclusion
● We have validated a slightly "deeper model" of
reasoning, using Pollock's puzzle with EBA.
● Advantages:
– defines justification operationally
– handles neutral prior probability
● Disadvantages:
– increased overload for the deep-modeling
– explosion of explanations
61. Further research
● Investigate other definitions of confirmation values
● Propose an analytical definition for attack/support
relations
● Integrate agent-role models into ASP
● Integrate EBA in agent architectures for diagnoser
agents
● Integrate Bayesian networks for prior probabilities