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Ishika Singh authored and Ishika Singh committed Mar 27, 2024
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<h2 class="title is-4"><span class="dprogprompt">TwoStep</span> multi-agent planning in AI2THOR</h2>
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<p>
We propose decomposing multi-agent planning problem into two single agent planning problems by leveraging LLM's commonsense and reasoning contextualized with humans operating together in diverse scenarios.
In particular, we consider a two-agent planning problem with a \helper\ and \main\ agent.
For a given problem $\mathrm{P}$, the \helper\ plans $\pi_h = \prod(i, g')$ to reach a subgoal state $g'$ from $\mathrm{P}$'s initial state $i$, using a planner $\prod$.
This subgoal state ($g'$) then serves as the initial state ($i' = E(i, \prod(s, g')$) for the \main\ agent plan $\pi_m = \prod(i', g)$ to reach the $\mathrm{P}$'s specified goal, $g$, where $E$ refers to \textit{Plan Execution}.
The \helper\ conceives $g'$ using 2 modules: \sggen\ to produce a possible helper subgoal in English - \engsg, and \sgtrans\ to translate English subgoal into PDDL format goal - \pddlsg.
We hypothesize that the commonsense reasoning abilities of LLMs can be leveraged in this context to predict \helper\ agent subgoals whose plan actions $\pi_h$ will be executable in parallel to those of the \main\ agent $\pi_m$, where latter takes for granted that the former will achieve the subgoal eventually.
In particular, we consider a two-agent planning problem with a helper and main agent.
For a given problem $\mathrm{P}$, the helper plans $\pi_h = \prod(i, g')$ to reach a subgoal state $g'$ from $\mathrm{P}$'s initial state $i$, using a planner $\prod$.
This subgoal state ($g'$) then serves as the initial state ($i' = E(i, \prod(s, g')$) for the main agent plan $\pi_m = \prod(i', g)$ to reach the $\mathrm{P}$'s specified goal, $g$, where $E$ refers to Plan Execution.
The helper conceives $g'$ using 2 modules: Subgoal Generator to produce a possible helper subgoal in English - English Subgoal, and Subgoal Translator to translate English subgoal into PDDL format goal - PDDL Subgoal.
We hypothesize that the commonsense reasoning abilities of LLMs can be leveraged in this context to predict helper agent subgoals whose plan actions $\pi_h$ will be executable in parallel to those of the main agent $\pi_m$, where latter takes for granted that the former will achieve the subgoal eventually.
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