We describe an implemented system that supports deep semantic NLU for controlling systems with multiple simulated robot agents. The system supports bidirectional communication for both human-agent and agent-agent inter-action. This interaction is achieved with the use of N-tuples, a novel form of Agent Communication Language using shared protocols with content expressing actions or intentions. The system's portability and flexibility is facilitated by its division into unchanging "core" and "application-specific" components.

Natural human dialogue often contains ambiguous or indirect speech. This poses a unique challenge to language understanding systems because comprehension requires going beyond what is said to what is implied. In this paper, we survey related work on the particularly challenging case of understanding non-conventional indirect speech acts, then propose a more generalizable rule rooted in building a mental model of the speaker. Finally, we discuss experimental evidence pointing to the cognitive plausibility of this rule.

Speakers frequently repair their speech, and listeners must therefore integrate information across ill-formed, often fragmentary inputs. Previous dialogue systems for human-robot interaction (HRI) have addressed certain problems in dialogue repair, but there are many problems that remain. In this paper, we discuss these problems from the perspective of Conversation Analysis, and argue that a more holistic account of dialogue repair will actually aid in the design and implementation of machine dialogue systems.

Raid bosses are the toughest fights in any massively multiplayer online game, but eventually players always find ways to crack them. Now developers are looking at ways to use cloud computing to give raid bosses a way of fighting back. During one of Microsoft's Game Stack developer livestreams yesterday, software engineer James Trott said that as developers are able to use the resources of the cloud to update and improve their games in real time, the real difficulty will be in not making them too hard. "We worked on a project with Microsoft tech last year for an MMO-like system for bosses that learn player behaviors in raid encounters," Trott said. "As raids go on and people find dominant strategies, the bosses adapt in near real time [and] detect the strategies players are implementing."