TY - GEN
T1 - Fast and Complete Symbolic Plan Recognition
AU - Avrahami-Zilberbrand, Dorit
AU - Kaminka, Gal A.
N1 - Place of conference:SCOTLAND
PY - 2005
Y1 - 2005
N2 - Recent applications of plan recognition face several
open challenges: (i) matching observations
to the plan library is costly, especially with complex
multi-featured observations; (ii) computing
recognition hypotheses is expensive. We present
techniques for addressing these challenges. First,
we show a novel application of machine-learning
decision-tree to efficiently map multi-featured observations
to matching plan steps. Second, we provide
efficient lazy-commitment recognition algorithms
that avoid enumerating hypotheses with every
observation, instead only carrying out bookkeeping
incrementally. The algorithms answer
queries as to the current state of the agent, as well as
its history of selected states. We provide empirical
results demonstrating their efficiency and capabilities
AB - Recent applications of plan recognition face several
open challenges: (i) matching observations
to the plan library is costly, especially with complex
multi-featured observations; (ii) computing
recognition hypotheses is expensive. We present
techniques for addressing these challenges. First,
we show a novel application of machine-learning
decision-tree to efficiently map multi-featured observations
to matching plan steps. Second, we provide
efficient lazy-commitment recognition algorithms
that avoid enumerating hypotheses with every
observation, instead only carrying out bookkeeping
incrementally. The algorithms answer
queries as to the current state of the agent, as well as
its history of selected states. We provide empirical
results demonstrating their efficiency and capabilities
UR - https://scholar.google.co.il/scholar?q=Fast+and+complete+symbolic+plan+recognition&btnG=&hl=en&as_sdt=0%2C5
M3 - Conference contribution
BT - IJCAI
ER -