A new framework for abm based on argumentative reasoning

A NEW FRAMEWORK FOR ABM BASED ON ARGUMENTATIVE REASONING

SIMONE GABBRIELLINI AND PAOLO TORRONI{SIMONE.GABBRIELLINI;PAOLO.TORRONI}@UNIBO.IT

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1giovedì 19 settembre 13

INTRODUCTION

• Argumentative approach to ABM

• Agents are socially embedded

• Agents exchange information by means of simulated dialogues.

• We argue that this approach:

• Allows a simple representation of actor’s reasoning,

• helps filling the gap between scholars that represent explicitly agent’s reasoning and scholars that do not.

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FIRST STREAM

• Models of the first stream share at least two common features:

• a network representation to mimic social embeddedness;

• a preference for mathematical, game theoretical or evolutionary computing techniques.

• It encompasses model of human hierarchies, trust and norm evolution, cooperation, cultural differentiation and collective behaviors, opinion dynamics and so on and so forth...

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SECOND STREAM

• The second stream focuses on how social agents should reason

• These models rely on formal logics and BDI frameworks to represent agent opinions, tasks and decision-making capabilities.

• It encompasses models of trust, cognitive representations and norms evolution and evaluation, and so on and so forth...

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ARGUMENTATIVE AGENTS

• Our approach is grounded on well established theories from social, cognitive, and computer science...

• Many degrees of freedom:

• Different embedding structures

• Different trust models

• Different ways of processing information

• The only pivotal point is to represent in format ion and reasoning with computational abstract argumentation.

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EMBEDDED AGENTS

• The basic idea of embeddedness comes from Granovetter’s hypothesis, w h i c h s t a t e s t h a t o u r acquaintances are less likely to be connected with each other than our close friends...

• Granovetter called this type of bridges “weak ties”, and demonstrated their importance in permitting the flow of resources, par ticularly information, between otherwise unconnected clusters - i.e. small worlds...

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EMBEDDED AGENTS

• We use a “caveman graph” to represent a situation where clusters are maximally dense...

• We then allow for two kind of structural settings:

1. each “cave” is disconnected from the others, thus agents can interact within their own cluster only;

2. a random number of bridges is added between caves, thus agents can interact occasionally with members of different caves. Even if our mechanism does not guarantee that all the caves become connected, on average the resulting networks exhibit small-world network characteristics...

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AGENT REASONING AND INTERACTION

• Accord ing to Merc ier & Sperber ’s argumentat ive theor y of reasoning , reasoning developed as a “tool” to convince others by means of arguments exchanged in dialogues

• Let’s see a brief sketch of how it works:

• Every time a bit of information is received, an addressee checks if the information is consistent with what she knows

• If this is the case, the bit is included in addressee’s knowledge, otherwise, she has to react to avoid cognitive dissonance

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• A d d r e s s e e f a c e s t w o alternatives:

• either to reject the new information (addressee does not trust the source enough), and possibly rebut;

• or to accep t the new information (addressee trusts the source) and revise his/her own beliefs;

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I claim x because y

???



• Both addressee and source may have to revise their own opinions while involved in such a turn-taking interaction, until:

• after revising, they both agree;

• they decide to stop arguing because they do not trust each other...

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ABSTRACT ARGUMENTATION

• An Argumentation Framework (AF) is defined as a pair ⟨A,R⟩, where:

• A is a set of arguments

• R is a binary attacks relation over arguments, R ⊆ A×A, with α → β ∈ R interpreted as “argument α attacks argument β.”

• In other words, an AF is a network of arguments, where directed links represent attack relations

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ABSTRACT ARGUMENTATION

• D1: My government cannot n e g o t i a t e w i t h y o u r government because your government doesn’t even recognize my government (a).

• D2: Your government doesn’t recognize my government either (b).

• D1: But your government is a terrorist government (c)...

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a

b

c


SEMANTIC EXTENSIONS

• Abstract argumentation analyzes AFs by means of semantics, i.e. set of rules used to identify “ c o h e r e n t ” s u b s e t s o f arguments.

• Semantics may range from very credulous to very skeptical ones.

• Each coherent set of arguments, according to the correspondent s e m a n t i c s , i s c a l l e d a n “extension” of A.

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a

b

c


SIMULATED DIALOGUES

• A simulated dialogue D starts with an “invitation to discuss” from A (source) to B (addressee), by picking a random argument β in her own extensions.

• IF: B evaluates β as coherent with his own AF , dialog stops: they already “agree”

• ELSE: if β is not included in any of B’s extensions, B faces an alternative:

• if he trusts A, he will revise his own opinions by adding the new informat ion and recalculating his semantic extensions

• if he does not trust A, he either will quit the discussion or, if he can, rebut α→β and wait for A’s reaction

• [So on and so forth until exit conditions...]

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attack

invitatio

n


AGAIN ON AGENT REASONING

• As a framework, several points are left open:

• we do no t commi t to any spec i fi c argumentation semantics

• we do not commit to any specific opinion revision mechanism.

• We also assume that agents rely on a trust model. Arguably, a realistic model of trust would to take into account the authoritativeness, rank and social status of the interlocutor.

• To date, our dialogue model is orthogonal to trust, we define trust thresholds statically but different trust models can be accommodated in the future.

• Furthermore, in our model information is either accepted or rejected...

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ABM AND ARGUMENTS: NETARG16


MODEL SCHEDULING

• At each time step, each agent A is asked to start a dialogue with one of her neighbors B, extracted at random, who could be restricted to the same cave or not, depending on the presence of bridges.

• The probability to “argue” with members of the same cave is higher than with out-cave neighbors, according to the fact that bridges are less activated than strong ties.

• A picks one random argument in her extensions and addresses B

• B replies following the simulated dialogues procedure already sketched

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OUTCOME MEASURE

• The opinion revision process gives raise also to a polarization effect at the population level

• Polar izat ion occur s when the population divides into a small number of factions with high internal consensus and strong disagreement between them.

• We measure the level of polarization Pt as the variance of the distribution of the AF distances dij,t, i.e. the AF distance between agents i and j at time t...

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EXPERIMENTAL RESULTS19


CONCLUSIONS

• Using a network representation at different levels (social embedding and information), we have built a framework for social agents where reasoning is explicitly represented.

• We used abstract argumentation and argumentative theory of reasoning to build agents that exchange information through simulated dialogues.

• We demonstrated that our approach is, in principle, sufficient to reproduce two macro behaviors embedded in Granovetter’s theory, i.e., the tendency to inclusion of weak ties and a competitive advantage for non-isolated caves...

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CONCLUSIONS

• Our framework explicitly models agents reasoning capabilities and can be applied to socially embedded and interacting agents.

• To the best of our knowledge, abstract argumentation has not been used for social simulation.

• Our proposal:

• represents a way for qua l i ta t i ve a p p r o a c h e s t o fi t A B M f o r m a l requirements...

• envisages possible new grounds for cross-fer tilization between the social and computer sciences...

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THANK YOU FOR YOUR ATTENTION!!!

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A new framework for abm based on argumentative reasoning

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Transcript of A new framework for abm based on argumentative reasoning