An AI system that is to create a story (autonomously or in interaction with human users) requires capabilities from many subfields of AI in order to create characters that themselves appear to act intelligently and believably in a coherent story world. Specifically, the system must be able to reason about the physical actions and verbal interactions of the characters as well as their perceptions of the world. Furthermore it must make the characters act believably--i.e. in a goal-directed yet emotionally plausible fashion. Finally, it must cope with (and embrace!) the dynamics of a multiagent environment where beliefs, sentiments, and goals may change during the course of a story and where plans are thwarted, adapted and dropped all the time. In this paper, we describe a representational and algorithmic framework for modelling such dynamic story worlds, Continual Multiagent Planning. It combines continual planning (i.e. an integrated approach to planning and execution) with a rich description language for modelling epistemic and affective states, desires and intentions, sensing and communication. Analysing story examples generated by our implemented system we show the benefits of such an integrated approach for dynamic plot generation.

We describe recent work on PECAS, an architecture for intelligent robotics that supports multi-modal interaction. Categories and Subject Descriptors I.2.8 [Computing Methodologies]: artificial intelligence:

Robots do not fully understand the world they are situated in. This includes what humans talk to them about. A fundamental problem is thus how a robot can clarify such a lack of understanding. This paper addresses the issue of how a robot can create a plan for resolving a need for clarification. It characterises situated clarification as an information need which may arise in any sensory-motoric modality required to interpret the situated context of the robot, or any deliberative modality referring to that context. It then focuses on how, once a clarification need has been identified, the robot can create a plan in which one or more modalities are used to resolve it. Modalities are involved on the basis of the types of information they can provide. These information types are identified in the ontologies the modalities use to interconnect their content with content of other modalities (via information fusion). We take a continual approach to planning and execution monitoring. This provides the ability to re-plan depending on modality availability and success in resolving (part of) a clarification need. We illustrate the implementation on several examples.

For a robot to be able to first understand and then achieve a human's goals, it must be able to reason about a) the context of the current situation (with respect to which it must interpret the human's commands) and b) the future world state (as intended by the human) and how to achieve it. Since humans express their intentions and plans using qualitative symbolic representations, robots must be enabled to reason and interact on the same representational level. In this paper, we describe the use of classical AI planning techniques for situation-aware interpretation and execution of human commands. We show how, based on a planning domain, a robot can be enabled to understand commands in natural language, plan for their situation-dependent realization and revise its plans based on new perceptions. We show the effectiveness of this approach in several HRI scenarios modeled as planning domains as well as with examples from a real robot system developed in the EU-funded CoSy project.