Argumentation and Data-Oriented Belief Revision: On the Two-Sided Nature of Epistemic Change

1. Argumentation andData-Oriented Belief Revision:On the Two-Sided Natureof Epistemic Change Fabio Paglieri CMNA IV University of Siena, Italy August 23-24, 2004 Cristiano Castelfranchi ISTC-CNR Roma, Italy Valencia, Spain

2. Introduction: Belief Revision and Argumentation 1st underlying claim:Belief revision (BR) and argumentation strategies (Arg) are best understood / should be studied within the same conceptual framework. • Belief revision = the way in which an agent changes its own mind, i.e. its own beliefs. • Argumentation = the way in which an agent changes other agents’ mind, by influencing their beliefs through communication. • Two sides (cognitive and social) of the same epistemic coin. • Related works: belief change and communication(Galliers 1992), belief revision and defeasible reasoning(Pollock, Gillies 2000; Falappa, Kern-Isberner, Simari 2002). • In CMNA IV: belief networks for modeling argumentation(Carofiglio), the role of beliefs, goals, and beliefs over goals in argumentation(Amgoud, Prade). 2nd underlying claim:In order to capture and model Arg, we need BR formalisms with a proper degree of structural complexity. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

3. Introducing Data-Oriented Belief Revision (DBR) Our model is based on a fundamental distinction between dataand beliefs. • Data are information available to the agent (i.e. gathered and stored in his mind), without and before any commitment to their reliability. • Beliefs are those data that the agent accepts as reliable bases for action, decision and specific reasoning tasks, e.g. inference, prediction and explanation. In this framework, data are selected (i.e. either accepted or rejected as beliefs) on the ground of their informational properties. Thus, changes over time in the outcomes of the selection process determine belief revision: in other words, BR is an emerging effect of data manipulation. We call this process Data-oriented Belief Revision (DBR). Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

4. Introducing Data-Oriented Belief Revision (DBR) • DBR is based on a conceptual model of epistemic processing far more complex than the original AGM scheme(Paglieri 2004): Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

5. Focusing Belief Selection Introducing Data-Oriented Belief Revision (DBR) Informational properties of data (Castelfranchi 1996; Paglieri 2004) are: • Relevance: a measure of the pragmatic utility of the datum, i.e. number and values of the (pursued) goals for which the datum is needed/useful; • Credibility: a measure of the number and values of all supporting data, contrasted with all conflicting data, down to external and internal sources; • Importance: a measure of the epistemic connectivity of the datum, i.e. number and values of the data that the agent will be forced to reconsider, should he reconsider that single one; • Likeability: a measure of the motivational appeal of the datum, i.e. number and values of the (pursued) goals that are directly fulfilled by that datum. • All of these are relational properties: thus, in DBR data are organized in networks, called Data Structures. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

6. Tolerant full realist Prudent open-minded realist Wishful thinking agent Condition C cf cf cf/ (1 – lf) Threshold k 0.5 0.6 0.8 Function F cf (cf + if + lf) / 3 cf (if + lf) Introducing Data-Oriented Belief Revision (DBR) • Belief selection: active data (i.e. data candidate as beliefs) are either accepted or rejected as beliefs on the basis of their credibility and/or importance and/or likeability, depending by the selection parameters of that particular agent (i.e. the informational properties that he considers most crucial in assessing the reliability of a given datum). Belief selection in DBR is performed by a mathematical system formed by a condition C, a threshold k and a function F. C and k together determine whether the datum is accepted or not as belief, while F assigns a value of strength to the corresponding belief (if any). Given a datum f with credibility cf, importance if and likeability lf, let B represent the agent’s belief set and Bsf the belief f with strength s. Then the general form of belief selection is: • If C(cf, if, lf) ≤ k then BsfB • If C(cf, if, lf) > k then BsfB with sf= F(cf, if, lf) Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

7. Support: f supports y (in symbols: fy) iff cycf, the credibility y of is directly proportional to the credibility of f.   • Contrast: f contrasts y (in symbols: fy) iff cy1/cf, the credibility y of is conversely proportional to the credibility of f.   • Union: f and y are united (in symbols: f&y) iff cf and cy, jointly (but not separately) determine the credibility of another datum.   Introducing Data-Oriented Belief Revision (DBR) • Data structures consist in data (nodes) linked together by characteristic relations (link). We define three basic relations: Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

8. Sa Ra a Sg d g b Rg Sb Rb Introducing Data-Oriented Belief Revision (DBR) • Example:Rhett was aware that his beloved Scarlett was supposed to take the US637 flight from Atlanta to New Orleans today, to pay a visit to her elderly wet nurse Mamy. Watching the news, Rhett is informed that there has been a terrible crash during the landing of that airplane and all passengers died. Frantic, Rhett calls Mamy, who tells him to get a grip of himself and stop blabbering, since Scarlett arrived safe and sound at her home two hours ago. DATA:a = Scarlett was on the flight US637 todayb = all passengers of today flight US637 died in a crash g = Scarlett is right now at Mamy’s home, safe and sound d = Scarlett is dead RELATIONS:{(a & b)d, g  d} Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

9. Modeling Argumentation in DBR • Representation results in Data Structures: • argumentation through plausibility; • Toulmin’s model of argument; • defeasible reasoning. • Expressivity results in DBR: [link] • contradiction management; • local vs. global argumentation strategies. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

10. a b f g d a e b f g d e Modeling Argumentation in DBR • A crucial feature in argumentation is plausibility, i.e. how much the claim of the arguer fit in with the pre-existing beliefs of the audience. • In DBR, plausibility-based arguments work on the importance of the datum that the arguer wants to defend. Importance of the datum can be manipulated employing two different cognitive strategies: • Self-evident datum: the new datum is presented as following from what the audience already knew – the datum has not yet been inferred, but it might have been, and the audience is likely to remark: «Sure! Of course! Obviously!» etc. • Explanatory datum: the new datum is presented as supporting and explaining data already available to the audience – since such explanation was missing so far, it produces reactions like: «Now I see! That’s why! I knew it!» etc. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

11. D = dataC = claimW = warrantQ = qualifierR = rebuttalB = backing Modeling Argumentation in DBR • Toulmin’s model can be easily represented in DBR as a peculiar Data Structure, as follows: Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

12. C Direct defeaters D W Premise defeaters Undercutting defeaters Modeling Argumentation in DBR • In DBR is it possible to represent three different types of defeaters: • direct (rebutting) defeaters = data contrasting the C node; • premise defeaters = data contrasting the D node; • undercutting defeaters = data contrasting the W node (i.e. rebuttals). • Example: John is innocent of the murder of his wife (claim) because he loved her much (data) and usually (qualifier) people do not murder the ones they love (warrant), since murder implies hate towards the victim (backing). • Direct defeater: «John had been seen shooting his wife». • Premise defeater: «John had a secret affaire with another woman». • Undercutting defeater: «Jealousy can make you kill the ones you love most». Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

13. Modeling Argumentation in DBR • While AGM approaches to BR exclude contradictions in principle from the agent’s belief set, argumentation proved to be a successful tool for handling contradictions(e.g. Amgoud, Cayrol 2002; De Rosis et al. 2000; Pollock, Gillies 2000). • In DBR we distinguish three types of mutually conflicting information: • data contrast: this is not at all problematic, but rather a beneficial relation, since it allows the agent to gather negative evidence on the contrasting claims; • implicit contradiction: the belief set of a resource-bounded agent is not closed under deduction, hence he might harbor (and ignore) some implicit contradictions – and making explicit such implicit contradictions is a well-known argumentative strategy; • explicit contradiction: co-occurring beliefs that mutually contradict each other. • The bottom-line is: contradictions need to be solved only if they arise at the level of beliefs. This is rare in DBR, but not impossible. However, the agent is not safe from contradiction by some benevolent law of nature, but he is rather equipped to handle contradictions efficiently. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

14. Modeling Argumentation in DBR • There is a relevant distinction between: • local argumentation: the agent aims to persuade his audience of a claim or set of claims, hence addressing specific beliefs (as in the examples discussed so far); • global argumentation: the agent aims to make the audience accept a different way of thinking, hence modifying their belief revision procedures (e.g. political campaign, commercial advertising, religious proselytism). • In DBR, local argumentation targets and manipulates specific nodes, relations or structures in the audience’s data network and/or belief set, while global argumentation targets the epistemic parametersof the audience, trying to change their setting according to the arguer’s goal. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

15. Modeling Argumentation in DBR “Unless I see in his hands the print of the nails, and place my finger in the mark of the nails, and place my hand in his side, I will not believe”. (St John, 20: 25) Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

16. Modeling Argumentation in DBR Meaning: you should be readier to believe without much evidence – i.e. changing your whole attitude in belief revision. GLOBAL ARGUMENTATION Have you believed because you have seen me? Blessed are those who have not seen and yet believe. Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

17. Modeling Argumentation in DBR Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

18. Current and Future Works • To refine the DBR model, e.g. by exploring in detail the assessment of data properties(Paglieri 2004), information update(Castelfranchi 1997; Fullam 2003), data mapping and inferential processing of beliefs. • To move towards implementation in agent-based cognitive and social simulation, e.g. within the AKIRA framework (Pezzulo, Calvi 2004), characterizing DBR itself as a distributed system(Dragoni, Giorgini 2003). • To compare our theoretical predictions over belief change and argumentation with empirical findings in experimental psychology. • To investigate the interplay between emotions, beliefs and arguments(Frijda et al. 2000; Paglieri 2004). • To explore the possible relationship between DBR and Truth-Maintenance Systems (TMS: see Doyle 1979; Huns, Bridgeland 1991). • To further detail the framing of Toulmin’s layout of argument in DBR(Paglieri, Castelfranchi 2005). • To provide more systematic connections with other argumentation models, especially within the MAS community (e.g. Capobianco, Chesñevar, Simari 2004). Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES

19. Thanks for your kind attention! Please contact: Fabio PaglieriUniversity of Siena, IT paglieri@media.unisi.it http://www.media.unisi.it/cirg/fp/paglieri.html Cristiano CastelfranchiISTC-CNR Roma, IT c.castelfranchi@istc.cnr.it http://www.istc.cnr.it/createhtml.php?nbr=62 Paglieri, Castelfranchi: Argumentation and Data-Oriented Belief Revision CMNA IV, August 23-24, 2004, Valencia, ES