Sharing online cultural experiences: An argument-based approach
Publication Type:
Conference PaperSource:
The 9th International Conference on Modeling Decisions for Artificial Intelligence, Springer-Verlag, Girona, Catalonia (2012)Keywords:
shared experiences; group decisions; argumentationAbstract:
This paper proposes a system that allows a group of human users to share their cultural experiences online, like buying together a gift from a museum or browsing simultaneously the collection of this museum. We show that such application involves two multiple criteria decision problems for choosing between different alternatives (e.g. possible gifts): one at the level of each user, and one at the level of the group for making joint decisions. The former is made manually by the users via the WeShare interface. This interface displays an image with tags re?ecting some features (criteria) of the image. Each user expresses then his opinion by rating the image and each tag. A user may change his choices in light of a report provided by his WeShare agent on the opinion of the group. Joint decisions are made in an automatic way. We provide a negotiation protocol which shows how they are reached. Both types of decisions are based on the notion of argument. Indeed, a tag which is liked by a user constitutes an argument pro the corresponding image whereas a tag which is disliked gives birth to a cons argument. These arguments may have different strengths since a user may express to what extent he likes/dislikes a given tag. Finally, the opinion analysis performed by a WeShare agent consists of aggregating the arguments of the users.
Personalizing Communication about Trust
t-DeLP: a temporal extension of the defeasible logic programming argumentative framework
Publication Type:
Conference PaperSource:
SUM 2011, Springer-Verlag, Volume 6929, Dayton (Ohio), USA, p.489-503 (2011)Keywords:
Defeasible logic programming; argumentation; temporal extensionAbstract:
The aim of this paper is to offer an argumentation-based defeasible logic that enables forward reasoning with time. We extend the $\DeLP$ logical framework by associating temporal parameters to literals. A temporal logic program is a set of temporal literals and durative rules. These temporal facts and rules combine to into durative arguments representing temporal processes, that permit us to reason defeasibly about future states. The corresponding notion of logical consequence, or warrant, is defined slightly different from that of $\DeLP$, due to the temporal aspects. As usual, this notion takes care of inconsistencies, and in particular we prove the consistency of any logical program whose strict part is consistent. Finally, we define and study a sub-class of arguments that seem appropriate to reason with natural processes, and suggest a modification to the framework that is equivalent to restricting the logic to this class of arguments.
Concept Convergence in Empirical Domains
Publication Type:
Conference PaperSource:
DS10: 13th International Conference on Discovery Science, p.281 - 295 (2010)Keywords:
Machine Learning; ArgumentationArgumentation-based Multiagent Induction
A Computational Method for Defeasible Argumentation Based on a Recursive Warrant Semantics
Publication Type:
Conference PaperSource:
12th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2010, Springer, Volume 6433, Bahía Blanca, Argentina, p. 40–49 (2010)Keywords:
defeasible argumentation; recursive semantics; computational aspectsAbstract:
In a recent paper [2] the authors have formalized a recursive semantics for warranted conclusions in a general defeasible argumentation framework based on a propositional logic. The warrant recursive semantics is based on a general notion of collective (non-binary) conflict among arguments allowing to ensure direct and indirect consistency properties. This general framework has also been extended with levels of defeasibility and with a level-wise recursive definition of warranted and blocked conclusions. In this paper we focus on the recursive semantics for the particular framework of Defeasible Logic Programming (DeLP) extended with levels of defeasibility, called RP-DeLP, for which we characterize programs with a unique output (extension) for warranted conclusions, and we design, for this type of programs, an algorithm for computing warranted conclu- sions in polynomial space and with an upper bound on complexity equal to P^NP
Towards a logical model of induction from examples and communication
Publication Type:
Conference PaperSource:
Artificial Intelligence Research and Development, CCIA'10, IOS Press, p.259-268 (2010)Keywords:
Induction; Logic; Argumentation; Machine LearningAbstract:
This paper focuses on a logical model of induction, and specifically of
the common machine learning task of inductive concept learning (ICL). We define
an inductive derivation relation, which characterizes which hypothesis can be
induced from sets of examples, and show its properties. Moreover, we will also
consider the problem of communicating inductive inferences between two agents,
which corresponds to the multi-agent ICL problem. Thanks to the introduced logical
model of induction, we will show that this communication can be modeled
using computational argumentation.
A characterization of collective conflict for defeasible argumentation
Publication Type:
Conference PaperSource:
Third International Conference on Computational Models of Argument, COMMA 2010, IOS Press, Volume 216, Desenzano del Garda, Italy, p.27-38 (2010)ISBN:
978-1-60750-618-8Abstract:
In this paper we define a recursive semantics for warrant in a general defeasible argumentation framework by formalizing a notion of collective (non-binary) con- flict among arguments. This allows us to ensure direct and indirect consistency (in the sense of Caminada and Amgoud) without distinguishing between direct and in- direct conflicts. Then, the general defeasible argumentation framework is extended by allowing to attach levels of preference to defeasible knowledge items and by providing a level-wise definition of warranted and blocked conclusions. Finally, we formalize the warrant recursive semantics for the particular framework of Pos- sibilistic Defeasible Logic Programming, characterize the unique output program property and design an efficient algorithm for computing warranted conclusions in polynomial space.
Argumentation-Based Information Exchange in Prediction Markets
On reusing other people’s experiences
Publication Type:
Journal ArticleSource:
Künstliche Intelligenz, Volume 21, p.18-23 (2009)Keywords:
CBRAbstract:
The Web is a vibrant environment for innovation in computer science, AI, and social interaction; these innovations come in such great number and speed that it is almost impossible to follow them. This paper will focus on some emerging aspects on the web that are a great opportunity and challenge for AI, specifically the large amount of records of experiences about the world that individual people share in the Web. I discuss a new approach that, instead of focusing on improving information access in the web, aims at supporting people to reuse other people's experiences, recorded in the web, in order to take more informed actions in the world.
