Abstract not found

In this paper, we study the completeness of the 0-approximation for action theories with incomplete information. We propose a sufficient condition for which an action theory under the 0-approximation semantics is complete with respect to the possible world semantics. We then introduce the notion of decisive sets of fluents, based on which an action theory can be modified into another action theory such that the modified action theory under the 0-approximation is complete with respect to the original theory. We present a polynomial time algorithm for computing decisive sets for action theories and use it in the development of a sound and complete conformant planner. Finally, we compare our planner with other state-of-the-art conformant planners.

Flexible general purpose robots need to tailor their visual processing to their task, on the fly. We propose a new approach to this within a planning framework, where the goal is to plan a sequence of visual operators to apply to the regions of interest (ROIs) in a scene. We pose the visual processing problem as a Partially Observable Markov Decision Process (POMDP). This requires probabilistic models of operator effects to quantitatively capture the unreliability of the processing actions, and thus reason precisely about trade-offs between plan execution time and plan reliability. Since planning in practical sized POMDPs is intractable we show how to ameliorate this intractability somewhat for our domain by defining a hierarchical POMDP. We compare the hierarchical POMDP approach with a Continual Planning (CP) approach. On a real robot visual domain, we show empirically that all the planning methods outperform naive application of all visual operators. The key result is that the POMDP methods produce more robust plans than either naive visual processing or the CP approach. In summary, we believe that visual processing problems represent a challenging and worthwhile domain for planning techniques, and that our hierarchical POMDP based approach to them opens up a promising new line of research.

We provide a general way to take forward-chaining planners for classical planning domains and conditionalize them, i.e., adapt them to generate policies for partially observable planning domains. For domain-configurable planners such as SHOP2, TLPlan, and TALplanner, our generalization technique preserves the ability to use domain knowledge to achieve highly efficient planning. We demonstrate this experimentally in two problem domains. The experiments compare PKS and MBP (two existing planners for partially observable planning) and CondSHOP2, a version of the HTN planner SHOP2 created by applying our conditionalization method. To our surprise, PKS and MBP could solve only the simplest problems in our test domains. In contrast, CondSHOP2 solved all of the test problems quite easily. This suggests that the ability to use domain knowledge may be not just desirable but indeed essential for solving large problems in partially observable domains.

The problem of planning dialog moves can be viewed as an instance of the more general AI problem of planning with incomplete information and sensing. Sensing actions complicate the planning process since such actions engender potentially infinite state spaces. We adapt the Linear Dynamic Event Calculus (LDEC) to the representation of dialog acts using insights from the PKS planner, and show how this formalism can be applied to the problem of planning mixedinitiative collaborative discourse. 1

Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the interdependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.

We aim at representing and reasoning about actions and (high level) programs over ontologies expressed in Description Logics. This is a critical issue that has resisted good solutions for a long time. In particular, while well-developed theories of actions and high-level programs exist in AI, e.g., the ones based on SitCalc, these theories do not apply smoothly to Description Logic ontologies, due to the profoundly non-definitorial nature of such ontologies (cf. cyclic TBoxes). Here we propose a radical solution: we assume a functional view of ontologies and see them as systems that allow for two kinds of operations: ask, which returns the (certain) answer to a query, and tell, which produces a new ontology as a result of the application of an atomic action. We base atomic actions on instance level update and instance level erasure on the ontology. Building on this functional view, we introduce Golog/ConGolog-like high-level programs on ontologies. This paper demonstrates the effectiveness of the approach in general, and presents the following specific results: we characterize the notion of single-step executability of such programs, devise methods for reasoning about sequences of actions, and present (nice) complexity results in the case where the ontology is expressed in DL-Lite. 1

We propose a framework, based on Linear Logic, for finding and executing plans that include dialogue with the aim of simplifying agent design. In particular, we provide a model that allows agents to be robust to unexpected events and failures, and supports significant reuse of agent specifications. Using Linear Logic as the foundational machinery improves upon previous dialogue systems by providing a clear underlying logical model for both planning and execution. The resulting framework has been implemented and several case studies have been considered. Further applications include human-computer interfaces as well as agent interaction in the semantic web.

Abstract — In this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical symbolic regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals. I.

Logical Filtering is the problem of tracking the possible states of a world (belief state) after a sequence of actions and observations. It is fundamental to applications in partially observable dynamic domains. This paper presents the first exact logical filtering algorithm that is tractable for all deterministic domains. Our tractability result is interesting because it contrasts sharply with intractability results for structured stochastic domains. The key to this advance lies in using logical circuits to represent belief states. We prove that both filtering time and representation size are linear in the sequence length and the input size. They are independent of the domain size if the actions have compact representations. The number of variables in the resulting formula is at most the number of state features. We also report on a reasoning algorithm (answering propositional questions) for our circuits, which can handle questions about past time steps (smoothing). We evaluate our algorithms extensively on AIplanning domains. Our method outperforms competing methods, sometimes by orders of magnitude. 1