Pascal Bercher

Advanced user assistance based on AI planning

Artificial Intelligence technologies enable the implementation of cognitive systems with advanced planning and reasoning capabilities. This article presents an approach to use hybrid planning - a method that combines reasoning about procedural knowledge and causalities - to provide user-centered assistance. Based on a completely declarative description of actions, tasks, and solution methods, hybrid planning allows for the generation of knowledge-rich plans of action. The information those plans comprise includes causal dependencies between actions on both abstract and primitive levels as well as information about their hierarchical and temporal relationships. We present the hybrid planning approach in detail and show its potential by describing the realization of various assistance functionalities based on complex cognitive processes like the generation, repair, and explanation of plans. Advanced user assistance is demonstrated by means of a practical application scenario where an innovative electronic support mechanism helps a user to operate a complex mobile communication device.

Companion-Technology: Towards User- and Situation-Adaptive Functionality of Technical Systems

The properties of multimodality, individuality, adaptability, availability, cooperativeness and trustworthiness are at the focus of the investigation of Companion Systems. In this article, we describe the involved key components of such a system and the way they interact with each other. Along with the article comes a video, in which we demonstrate a fully functional prototypical implementation and explain the involved scientific contributions in a simplified manner. The realized technology considers the entire situation of the user and the environment in current and past states. The gained knowledge reflects the context of use and serves as basis for decision-making in the presented adaptive system.

Pattern database heuristics for fully observable nondeterministic planning

When planning in an uncertain environment, one is often interested in finding a contingent plan that prescribes appropriate actions for all possible states that may be encountered during the execution of the plan. We consider the problem of finding strong cyclic plans for fully observable nondeterministic (FOND) planning problems. The algorithm we choose is LAO*, an informed explicit state search algorithm. We investigate the use of pattern database (PDB) heuristics to guide LAO* towards goal states. To obtain a fully domain-independent planning system, we use an automatic pattern selection procedure that performs local search in the space of pattern collections. The evaluation of our system on the FOND benchmarks of the Uncertainty Part of the International Planning Competition 2008 shows that our approach is competitive with symbolic regression search in terms of problem coverage, speed, and plan quality.

Language classification of hierarchical planning problems

Theoretical results on HTN planning are mostly related to the plan existence problem. In this paper, we study the structure of the generated plans in terms of the language they produce. We show that such languages are always context-sensitive. Furthermore we identify certain subclasses of HTN planning problems which generate either regular or context-free languages. Most importantly we have discovered that HTN planning problems, where preconditions and effects are omitted, constitute a new class of languages that lies strictly between the context-free and context-sensitive languages.

Using State-Based Planning Heuristics for Partial-Order Causal-Link Planning

We present a technique which allows partial-order causal-link (POCL) planning systems to use heuristics known from state-based planning to guide their search.

Companion-Technology: An Overview

Companion-technology is an emerging field of cross-disciplinary research. It aims at developing technical systems that appear as “Companions” to their users. They serve as co-operative agents assisting in particular tasks or, in a more general sense, even give companionship to humans. Overall, Companion-technology enables technical systems to smartly adapt their services to individual users’ current needs, their requests, situation, and emotion. We give an introduction to the field, discuss the most relevant application areas that will benefit from its developments, and review the related research projects.

The Interplay of User-Centered Dialog Systems and AI Planning

Technical systems evolve from simple dedicated task solvers to cooperative and competent assistants, helping the user with increasingly complex and demanding tasks. For this, they may proactively take over some of the users responsibilities and help to find or reach a solution for the user’s task at hand, using e.g., Artificial Intelligence (AI) Planning techniques. However, this intertwining of user-centered dialog and AI planning systems, often called mixed-initiative planning (MIP), does not only facilitate more intelligent and competent systems, but does also raise new questions related to the alignment of AI and human problem solving. In this paper, we describe our approach on integrating AI Planning techniques into a dialog system, explain reasons and effects of arising problems, and provide at the same time our solutions resulting in a coherent, userfriendly and efficient mixed-initiative system. Finally, we evaluate our MIP system and provide remarks on the use of explanations in MIP-related phenomena.

On Delete Relaxation in Partial-Order Causal-Link Planning

We prove a new complexity result for Partial-Order Causal-Link (POCL) planning which shows the hardness of refining a search node (i.e., a partial plan) to a valid solution given a delete effect-free domain model. While the corresponding decision problem is known to be polynomial in state-based search (where search nodes are states), it turns out to be intractable in the POCL setting. Since both of the currently best-informed heuristics for POCL planning are based on delete relaxation, we hope that our result sheds some new light on the problem of designing heuristics for POCL planning. Based on this result, we developed a new variant of one of these heuristics which incorporates more information of the current partial plan. We evaluate our heuristic on several domains of the early International Planning Competitions and compare it with other POCL heuristics from the literature.

Interview with David E. Smith

David E. Smith is a senior Researcher in the Intelligent Systems Division at NASA Ames Research Center. He received his Ph.D. in 1985 from Stanford University, and spent time as a Research Associate at Stanford, a Scientist at the Rockwell Palo Alto Science Center, and a Visiting Scholar at the University of Washington before joining NASA in 1997. Beginning in 1999, he served as the lead of the 18 member planning and scheduling group at NASA Ames for 6 years before abdicating to devote more time to research. Much of his research has focused on pushing the boundaries of AI planning technology to handle richer models of time, concurrency, exogenous events, uncertainty, and oversubscription. Smith served as an Associate Editor for the Journal of Artificial Intelligence Research (JAIR) from 2001–2004, and as Guest Editor for the JAIR Special Issue and Special Track on the 3rd and 4th International Planning Competitions. He served on the JAIR Advisory Board 2004–2007. Smith was recognized as a AAAI Fellow in 2005, and served on the AAAI Executive Council 2007–2010. KI Would you like to tell us about your journey to NASA and your personal motivation for this?

User Involvement in Collaborative Decision-Making Dialog Systems

Mixed-initiative assistants are systems that support humans in their decision-making and problem-solving capabilities in a collaborative manner. Such systems have to integrate various artificial intelligence capabilities, such as knowledge representation, problem solving and planning, learning, discourse and dialog, and human-computer interaction. These systems aim at solving a given problem autonomously for the user, yet involve the user into the planning process for a collaborative decision-making, to respect e.g. user preferences. However, how the user is involved into the planning can be framed in various ways, using different involvement strategies, varying e.g. in their degree of user freedom. Hence, here we present results of a study examining the effects of different user involvement strategies on the user experience in a mixed-initiative system.