Adaptive Rich Media Presentations via Preference-Based Constrained Optimization

Personalization and adaptation of multi-media messages are well known and well studied problems. Ideally, each message should reflect its recipient\u27s interests, device capabilities, and network conditions. Such personalization is more difficult to carry out given a compound multi-media presentation containing multiple spatially and temporally related elements. This paper describes a novel formal, yet practical approach, and an implemented system prototype for authoring and adapting compound multi-media presentations. Our approach builds on recent advances in preference specification and preferences-based constrained optimization techniques

Planning with Concurrent Interacting Actions

In order to generate plans for agents with multiple actuators or agent teams, we must be able to represent and plan using concurrent actions with interacting effects. Historically, this has been considered a challenging task that could require a temporal planner. We show that, with simple modifications, the STRIPS action representation language can be used to represent concurrent interacting actions. Moreover, current algorithms for partial-order planning require only small modifications in order to handle this language and produce coordinated multiagent plans. These results open the way to partial order planners for cooperative multiagent systems. AI [8]—very little research addresses the MAP problem.2