Ascribing and Weighting Beliefs in Deceptive Information Exchanges

Carofiglio, Valeria; Rosis, Fiorella de; Castelfranchi, Cristiano

doi:10.1007/3-540-44566-8_27

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…In artificial intelligence, there have been studies specifically concerned with deception. Modelling suspicion in such a society of agents that deception may occur in it, is the subject of [15,16,31]. Sergot [103] is concerned with modelling unreliable and untrustworthy agent behaviour.…”

Section: The Auranzeb Project Under the Plotinus Umbrellamentioning

confidence: 99%

Epistemic formulae, argument structures, and a narrative on identity and deception: a formal representation from the AJIT subproject within AURANGZEB

Nissan

2008

Ann Math Artif Intell

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The modelling of deception is an active field in artificial intelligence. Episodic formulae were introduced, applied and refined in several previous articles, some of them concerned with identity, and some, with tasks of guessing agents' minds. The AURANGZEB project is specifically concerned with combining this kind of representation with argument structure data (in particular, Toulmin's). Two previous articles were concerned with the MURAD subproject, whereas this paper introduces the AJIT model. In MURAD, the kind of deception consisted of having a victim misunderstand a perpetrator's real intentions. In AJIT, instead, a powerful agent is outsmarted by means of exchanged identities.

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Section: The Auranzeb Project Under the Plotinus Umbrellamentioning

confidence: 99%

Epistemic formulae, argument structures, and a narrative on identity and deception: a formal representation from the AJIT subproject within AURANGZEB

Nissan

2008

Ann Math Artif Intell

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“…McKenzie and colleagues [17] discuss the potential benefits of deceptive agents as training partners that help the user to recognize malicious intent, but do not present any implementation. Castelfranchi and Poggi [5] developed a theory of deception in communication which has grounded prototyping of a deception modeling tool in which both the deceiver and the receiver of the message are modeled [3,6]. The issue of deception has also been addressed in the area of conversational systems [15] and in the area of multi-agent systems where different strategies of deception and their effects on the interaction of agents are explored [4,28].…”

Section: Introductionmentioning

confidence: 99%

Catch me if you can

Rehm

André

2005

Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems

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Embodied conversational agents become more and more realistic concerning their conversational and their nonverbal behaviors. But if the information conveyed nonverbally exhibits clues that are not consistent with the verbal part of an agent's action, how will the user react to such a discrepancy? Masking ones real emotions with a smile is a naturally occuring example of such a discrepancy. But such masks are often deficient and thus subtle clues of lying and deceiving manifest themselves in facial expressions. The questions is how users will react to these clues if they are conveyed by an agent. Will they render an application unattractive or on the contrary more human-like? In this paper, we examine such facial clues to deception and present the results of two empirical studies: i.) lies in monologues by a talking head presenting movies, ii.) lies in an interactive game of dice.

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“…If A has not dealt with D before, or A has not dealt with D in regard to either O or C, A must estimate probabilities. [9] notes this in modeling deception and tries a different approach, but we do not see this is necessary since one can use the results of a running intrusion-detection system if enough data has been collected about a user, and otherwise "statistical inheritance" with known marginal statistics on generalizations D' of D, O' of O, and C' of C. If there is more than one such D', O', or C' we need to take a weighted average of their predictions, where typically the weight is the inverse of the corresponding set size (the number of things in the real world to which the class corresponds, since larger classes are further in properties from the target class). Such reasoning can be fallacious, as for instance when we have had one bad experience with a Microsoft product and we generalize to all Microsoft products, or when we have had one bad experience with a person of one ethnic group and we generalize to all members of that group.…”

Section: The Strength Of Attacker Hypothesesmentioning

confidence: 99%

“…[10] and [19] provide ideas for modeling attacks on computer systems, the former suggesting some interesting ideas about deception in defense. Work on multi-agent systems is starting to examine societies of deceptive agents [3,9,30]. [4] identifies six basic methods of deception: masking, repackaging, dazzling, mimicking, inventing, and decoying.…”

Section: Introductionmentioning

confidence: 99%

A model of deception during cyber-attacks on information systems

Rowe

IEEE First Symposium onMulti-Agent Security and Survivability, 2004

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Deception is a classic INTRODUCTIONDeception is an important two-agent psychological phenomenon with many applications to information security. Deception can occur offensively, as when attackers try to fool our information systems into giving away secrets or destroying themselves, or it can occur defensively, as when a computer pretends by exaggerated processing delays to succumb to a denial-of-service attack so the attacker goes away. Little systematic analysis has been paid to the concept of deception in information systems, however. Most writers assume methods of deception are intuitively obvious or comprise only a few categories. But there are many possible kinds of deception, some quite subtle. And deceptions can be automated. So it is important in defense of our information systems that we develop a better theory of deception and ways to translate it into security practices.

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Ascribing and Weighting Beliefs in Deceptive Information Exchanges

Cited by 10 publications

References 5 publications

Epistemic formulae, argument structures, and a narrative on identity and deception: a formal representation from the AJIT subproject within AURANGZEB

Epistemic formulae, argument structures, and a narrative on identity and deception: a formal representation from the AJIT subproject within AURANGZEB

Catch me if you can

A model of deception during cyber-attacks on information systems

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