Planning and Proof Planning

. The paper adresses proof planning as a specific AI planning. It describes some peculiarities of proof planning and discusses some possible cross-fertilization of planning and proof planning. 1 Introduction Planning is an established area of Artificial Intelligence (AI) whereas proof planning introduced by Bundy in [2] still lives in its childhood. This means that the development of proof planning needs maturing impulses and the natural questions arise What can proof planning learn from its Big Brother planning?' and What are the specific characteristics of the proof planning domain that determine the answer?'. In turn for planning, the analysis of approaches points to a need of mature techniques for practical planning. Drummond [8], e.g., analyzed approaches with the conclusion that the success of Nonlin, SIPE, and O-Plan in practical planning can be attributed to hierarchical action expansion, the explicit representation of a plan's causal structure, and a very simple form of propo..

Graph-based task libraries for robots: generalization and autocompletion

In this paper, we consider an autonomous robot that persists over time performing tasks and the problem of providing one additional task to the robot's task library. We present an approach to generalize tasks, represented as parameterized graphs with sequences, conditionals, and looping constructs of sensing and actuation primitives. Our approach performs graph-structure task generalization, while maintaining task ex- ecutability and parameter value distributions. We present an algorithm that, given the initial steps of a new task, proposes an autocompletion based on a recognized past similar task. Our generalization and auto- completion contributions are eective on dierent real robots. We show concrete examples of the robot primitives and task graphs, as well as results, with Baxter. In experiments with multiple tasks, we show a sig- nicant reduction in the number of new task steps to be provided

Parameterized Complexity Results for Plan Reuse

Planning is a notoriously difficult computational problem of high worst-case complexity. Researchers have been investing significant efforts to develop heuristics or restrictions to make planning practically feasible. Case-based planning is a heuristic approach where one tries to reuse previous experience when solving similar problems in order to avoid some of the planning effort. Plan reuse may offer an interesting alternative to plan generation in some settings. We provide theoretical results that identify situations in which plan reuse is provably tractable. We perform our analysis in the framework of parameterized complexity, which supports a rigorous worst-case complexity analysis that takes structural properties of the input into account in terms of parameters. A central notion of parameterized complexity is fixed-parameter tractability which extends the classical notion of polynomial-time tractability by utilizing the effect of structural properties of the problem input. We draw a detailed map of the parameterized complexity landscape of several variants of problems that arise in the context of case-based planning. In particular, we consider the problem of reusing an existing plan, imposing various restrictions in terms of parameters, such as the number of steps that can be added to the existing plan to turn it into a solution of the planning instance at hand.Comment: Proceedings of AAAI 2013, pp. 224-231, AAAI Press, 201

Plan stability: replanning versus plan repair

The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the {em stable} repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning

A cooperative planning algorithm to improve performance in Web domains

Proceeding of: IEEE International Conference on Systems, Man and Cybernetics (SMC-2002), 6-9 Oct. 2002, Hammamet, TunezIn this paper, we present MAPWEB, a multiagent framework that integrates planning agents and Web information retrieval agents. The goal of this framework is to deal with problems that require planning with information to be gathered from the Web. Because of flexibility and efficiency reasons, MAPWEB decouples planning from information gathering by splitting a planning problem into two parts: solving an abstract problem and validating and completing abstract solutions by means of information gathering. We focus on the planning process in order to improve its efficiency. There are two ways of improving the efficiency of the MAPWEB planning algorithm: by accelerating the planning process itself and by storing previously solved planning problems. The first has been achieved by designing a cooperative planning algorithm that allows a set of planning agents to share plans and cooperate while planning to increase efficiency. The second is currently being designed and developed to allow planning agents to reuse the acquired knowledge. Finally, this paper presents experimental evaluation of the cooperative planning process when it is used by the planning agents.Publicad

A Roadmap for Self-Evolving Communities

Self-organisation and self-evolution is evident in physics, chemistry, biology, and human societies. Despite the existing literature on the topic, we believe self-organisation and self-evolution is still missing from the IT tools (whether online or offline) we are building and using. In the last decade, human interactions have been moving more and more towards social media. The time we spend interacting with others in virtual communities and networks is tremendous. Yet, the tools supporting those interactions remain rigid. This position paper argues the need for self-evolving software-enabled communities, and proposes a roadmap for achieving this required self-evolution. The proposal is based on building normative-based communities, where community interactions are regulated by norms and community members are free to discuss and modify their community's norms. The evolution of communities is then dictated by the evolution of its norms.Peer Reviewe

Merging plans with incomplete knowledge about actions and goals through an agent-based reputation system

Managing transition plans is one of the major problems of people with cognitive disabilities. Therefore, finding an automated way to generate such plans would be a helpful tool for this community. In this paper we have specifically proposed and compared different alternative ways to merge plans formed by sequences of actions of unknown similarities between goals and actions executed by several operator agents which cooperate between them applying such actions over some passive elements (node agents) that require additional executions of another plan after some time of use. Such ignorance of the similarities between plan actions and goals would justify the use of a distributed recommendation system that would provide an useful plan to be applied for a certain goal to a given operator agent, generated from the known results of previous executions of different plans by other operator agents. Here we provide the general framework of execution (agent system), and the different merging algorithms applied to this problem. The proposed agent system would act as an useful cognitive assistant for people with intelectual disabilities such as autism

Learning to solve planning problems efficiently by means of genetic programming

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.Publicad