Over the past few years, a new constraint-based formalism for temporal reasoning has been developed to represent and reason about Disjunctive Temporal Problems (DTPs). The class of DTPs is significantly more expressive than other problems previously studied in constraint-based temporal reasoning. In this paper we present a new algorithm for DTP solving, called Epilitis, which integrates strategies for efficient DTP solving from the previous literature, including conflict-directed backjumping, removal of subsumed variables, and semantic branching, and further adds no-good recording as a central technique. We discuss the theoretical and technical issues that arise in successfully integrating this range of strategies with one another and with no-good recording in the context of DTP solving. Using an implementation of Epilitis, we explore the effectiveness of various combinations of strategies for solving DTPs, and based on this analysis we demonstrate that Epilitis can achieve a nearly two order-of-magnitude speed-up over the previously published algorithms on benchmark problems in the DTP literature.

... an opportunity for the design of intelligent technology. This paper focuses on one type of assistive technology, cognitive orthotics, which can help people adapt to cognitive declines and continue satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Existing cognitive orthotics mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, we describe Autominder, a system we have designed that uses AI planning and plan management technology to carefully model an individual's daily plans, attend to and reason about the execution of those plans, and make flexible and adaptive decisions about when it is most appropriate to issue reminders. The paper concentrates on one of Autominder's three main components, the Plan Manager; other papers in this volume describe its other components (Colbry, Peintner, & Pollack 2002; McCarthy & Pollack 2002).

This paper * discusses the specifics of planning in multiagent environments. It presents the formal framework MAPL ("maple") for describing multiagent planning domains. MAPL allows to describe both qualitative and quantitative temporal relations among events, thus subsuming the temporal models of both PDDL 2.1 and POP. Other features are different levels of control over actions, modeling of agents ' ignorance of facts, and plan synchronization with communicative actions. For single-agent planning in multiagent domains, we present a novel forward-search algorithm synthesizing MAPL's partially ordered temporal plans. Finally, we present a general distributed algorithm scheme for solving MAPL problems with several coordinating planners. These different contributions are intended as as step

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences. 1.

In order for an autonomous agent to successfully complete its mission, the agent must be able to quickly replan on the fly, as unforeseen events arise in the environment. This is enabled through the use of temporally flexible plans, which allow the agent to adapt to execution uncertainties, by not over committing to timing constraints, and through continuous planners, which are able to replan at any point when the current plan fails. To achieve both of these requirements, planners must have the ability to reason quickly about timing constraints. We enable continuous, temporally flexible planning through a temporal consistency algorithm (ITC), which supports incremental consistency testing on a new type of disjunctive temporal constraint network, the Temporal Plan Network (TPN), and supports focused search through incremental conflict extraction. The ITC algorithm combines the speed of shortest-path algorithms known to network optimization with the spirit of incremental algorithms such as Incremental A * and those used within truth maintenance systems (TMS). Empirical studies of ITC applied to the Kirk temporal planner demonstrate an order of magnitude speed increase on cooperative air vehicle scenarios and on randomly generated plans.

In this paper, we focus on extending the expressive power of constraint-based temporal reasoning formalisms. We begin with the well-known Simple Temporal Problem with Uncertainty, and incorporate three extensions: prior observability, in which the values of uncontrollable events become known prior to their actual occurrence; partial shrinkage, in which an observation event triggers the reduction of a contingent temporal interval; and a generalization of partial shrinkage to requirement links, making it possible to express certain types of uncertainty that may arise even when the time points in a problem are themselves fully controllable. We describe levels of controllability in the resulting formalism, the Generalized STPU, and relate this formalism to related developments in disjunctive temporal reasoning. Throughout, we motivate our approach with simple, real-world examples that illustrate the limitations of existing formalisms and the flexibility of our proposed extensions. 1

In this paper we extend dynamic controllability of temporally-flexible plans to temporally-flexible reactive programs. We consider three reactive programming language constructs whose behavior depends on runtime observations; conditional execution, iteration, and exception handling. Temporally-flexible reactive programs are distinguished from temporally-flexible plans in that program execution is conditioned on the runtime state of the world. In addition, exceptions are thrown and caught at runtime in response to violated timing constraints, and handled exceptions are considered successful program executions. Dynamic controllability corresponds to a guarantee that a program will execute to completion, despite runtime constraint violations and uncertainty in runtime state. An algorithm is developed which frames the dynamic controllability problem as an AND/OR search tree over possible program executions. A key advantage of this approach is the ability to enumerate only a subset of possible program executions that guarantees dynamic controllability, framed as an AND/OR solution subtree.

Conditional Temporal Problems (CTPs) can deal simultaneously with uncertainty and temporal constraints, allowing for the representation of temporal and conditional plans. CTPPs generalize CTPs by adding preferences to the temporal constraints and by allowing fuzzy thresholds for the occurrence of some events. Here we focus on dynamic consistency of CTPPs, the most useful notion of consistency in practice. We describe an algorithm which allows for testing if a CTPP is dynamically consistent and we study its complexity. Simple Temporal Problems with Preferences and Uncertainty (STPPUs) are another for-malism to model temporal constraints where preference and uncertainty coexist. While uncertainty is CTPPs is modeled via conditions on the execution of variables, in STPPUs it is modelled by means of events whose occurrence time is not known. We con-sider the relation between CTPPs and STPPUs and we show that the former framework is at least as ex-pressive as the second one. Such a result is obtained by providing a polynomial mapping from STPPUs to CTPPs.

Abstract. Integrated modeling of temporal and logical constraints is important for solving real-life planning and scheduling problems. Logical constrains extend the temporal formalism by reasoning about alternative activities in plans/schedules. Temporal Networks with Alternatives (TNA) were proposed to model alternative and parallel processes, however the problem of deciding which activities can be consistently included in such networks is NP-complete. Therefore a tractable subclass of Temporal Networks with Alternatives was proposed. This paper shows formal properties of these networks where precedence constraints are assumed. Namely, an algorithm that effectively recognizes whether a given network belongs to the proposed sub-class is studied and the proof of tractability is given by proposing a constraint model where global consistency is achieved via arc consistency.

This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior work developed methods for dynamically dispatching Simple Temporal Networks, and further research enriched the expressiveness of the plans executives could handle, including discrete choices, which are the focus of this work. However, in some approaches to date, these additional choices induce significant storage or latency requirements to make flexible execution possible. Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation. We leverage the concepts of labels and environments, taken from prior work in Assumptionbased Truth Maintenance Systems (ATMS), to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or storage. Our labeling and maintenance scheme, called the Labeled Value Set Maintenance System, is distinguished by its focus on properties fundamental to temporal problems, and, more generally, weighted graph algorithms. In particular, the maintenance system focuses on maintaining a minimal representation of non-dominated constraints. We benchmark Drake’s performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices. 1.