SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning
Table 3: Performance Benchmark Details. We detail number of agents, desires, and the NL-I details of the benchmark scenarios including how many questions were asked, statements made, facts overheard by agents, and parser failures. We observe, as expected, that as the number of agents and desires increases, the amount of information gained from overhearing nearby agents increases.
['Scene', 'Agents', 'Desires', 'Statements', 'Questions', 'Facts Overheard', 'Parser failures']
In addition to the results reported in section 6 of the main document, we evaluated the algorithm’s performance as a function of domain size and number of agents. Consistent with prior propositional planning approaches, our algorithm scales linearly in the number of agents and exponentially in the size of the problem domain.