id: 06061103
dt: a
an: 06061103
au: Emele, Chukwuemeka David; Norman, Timothy J.; Guerin, Frank; Parsons, Simon
ti: On the benefits of argumentation-derived evidence in learning policies.
so: McBurney, Peter (ed.) et al., Argumentation in multi-agent systems. 7th
    international workshop, ArgMAS 2010, Toronto, ON, Canada, May 10, 2010.
    Revised, selected and invited papers. Berlin: Springer (ISBN
    978-3-642-21939-9/pbk). Lecture Notes in Computer Science 6614. Lecture
    Notes in Artificial Intelligence, 86-104 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: argumentation; machine learning; policies; norms; evidence
ci: 
li: doi:10.1007/978-3-642-21940-5_6
ab: Summary: An important and non-trivial factor for effectively developing and
    resourcing plans in a collaborative context is an understanding of the
    policy and resource availability constraints under which others
    operate. We present an efficient approach for identifying, learning and
    modeling the policies of others during collaborative problem solving
    activities. The mechanisms presented in this paper will enable agents
    to build more effective argumentation strategies by keeping track of
    who might have, and be willing to provide the resources required for
    the enactment of a plan. We argue that agents can improve their
    argumentation strategies by building accurate models of others’
    policies regarding resource use, information provision, etc. In a set
    of experiments, we demonstrate the utility of this novel combination of
    techniques through empirical evaluation, in which we demonstrate that
    more accurate models of others’ policies (or norms) can be developed
    more rapidly using various forms of evidence from argumentation-based
    dialogue.
rv: