Automated planning of simple persuasion dialogues

TY - CHAP

T1 - Automated planning of simple persuasion dialogues

AU - Black, Elizabeth

AU - Coles, Amanda

AU - Bernardini, Sara

PY - 2014

Y1 - 2014

N2 - We take a simple form of non-adversarial persuasion dialogue in which one participant (the persuader) aims to convince the other (the responder) to accept the topic of the dialogue by asserting sets of beliefs. The responder replies honestly to indicate whether it finds the topic to be acceptable (we make no prescription as to what formalism and semantics must be used for this, only assuming some function for determining acceptable beliefs from a logical knowledge base). Our persuader has a model of the responder, which assigns probabilities to sets of beliefs, representing the likelihood that each set is the responder's actual beliefs. The beliefs the persuader chooses to assert and the order in which it asserts them (i.e. its strategy) can impact on the success of the dialogue and the success of a particular strategy cannot generally be guaranteed (because of the uncertainty over the responder's beliefs). We define our persuasion dialogue as a classical planning problem, which can then be solved by an automated planner to generate a strategy that maximises the chance of success given the persuader's model of the responder; this allows us to exploit the power of existing automated planners, which have been shown to be efficient in many complex domains. We provide preliminary results that demonstrate how the efficiency of our approach scales with the number of beliefs.

AB - We take a simple form of non-adversarial persuasion dialogue in which one participant (the persuader) aims to convince the other (the responder) to accept the topic of the dialogue by asserting sets of beliefs. The responder replies honestly to indicate whether it finds the topic to be acceptable (we make no prescription as to what formalism and semantics must be used for this, only assuming some function for determining acceptable beliefs from a logical knowledge base). Our persuader has a model of the responder, which assigns probabilities to sets of beliefs, representing the likelihood that each set is the responder's actual beliefs. The beliefs the persuader chooses to assert and the order in which it asserts them (i.e. its strategy) can impact on the success of the dialogue and the success of a particular strategy cannot generally be guaranteed (because of the uncertainty over the responder's beliefs). We define our persuasion dialogue as a classical planning problem, which can then be solved by an automated planner to generate a strategy that maximises the chance of success given the persuader's model of the responder; this allows us to exploit the power of existing automated planners, which have been shown to be efficient in many complex domains. We provide preliminary results that demonstrate how the efficiency of our approach scales with the number of beliefs.

U2 - 10.1007/978-3-319-09764-0_6

DO - 10.1007/978-3-319-09764-0_6

M3 - Conference paper

SN - 9783319097633

T3 - Lecture Notes in Computer Science

SP - 87

EP - 104

BT - Computational Logic in Multi-Agent Systems

A2 - Bulling, Nils

A2 - van der Torre, Leendert

A2 - Villata, Serena

A2 - Jamroga, Wojtek

A2 - Vasconcelos, Waberto

PB - Springer Berlin Heidelberg

ER -