Many planning techniques have been developed to allow autonomous systems to act and make decisions based on their perceptions of the environment. Among these techniques, HTN ({\it Hierarchical Task Network}) planning is one of the most used in practice. Unlike classical approaches of planning. HTN operates by decomposing task into sub-tasks until each of these sub-tasks can be achieved an action. This hierarchical representation provide a richer representation of planning problems and allows to better guide the plan search and provides more knowledge to the underlying algorithms. In this paper, we propose a new approach of HTN planning in which, as in conventional planning, we instantiate all planning operators before starting the search process. This approach has proven its effectiveness in classical planning and is necessary for the development of effective heuristics and encoding planning problems in other formalism such as CSP or SAT. The instantiation is actually used by most modern planners but has never been applied in an HTN based planning framework. We present in this article a generic instantiation algorithm which implements many simplification techniques to reduce the process complexity inspired from those used in classical planning. Finally we present some results obtained from an experimentation on a range of problems used in the international planning competitions with a modified version of SHOP planner using fully instantiated problems.

In this paper, we introduce a generic and fresh model for distributed planning called "Distributed Planning Through Graph Merging" ({\sf DPGM}). This model unifies the different steps of the distributed planning process into a single step. Our approach is based on a planning graph structure for the agent reasoning and a CSP mechanism for the individual plan extraction and the coordination. We assume that no agent can reach the global goal alone. Therefore the agents must cooperate, {\it i.e.,} take in into account potential positive interactions between their activities to reach their common shared goal. The originality of our model consists in considering as soon as possible, {\it i.e.,} in the individual planning process, the positive and the negative interactions between agents activities in order to reduce the search cost of a global coordinated solution plan.

We introduce in this paper a new CP-based approach to support errors location in a program for which a counter-example is available, i.e. an instantiation of the input variables that violates the post-condition. To provide helpful information for error location, we generate a constraint system for the paths of the CFG (Control Flow Graph) for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. We extend the algorithm proposed by Liffiton and Sakallah \cite{LiS08} to handle programs with numerical statements more efficiently. We present preliminary experimental results that are quite encouraging.

We proposed in previous papers an extension and an implementation of the STROBE model, which regards the Agents as Scheme interpreters. These Agents are able to interpret messages in a dedicated environment including an interpreter that learns from the current conversation therefore representing evolving meta-level Agent's knowledge. When the Agent's interpreter is a nondeterministic one, the dialogues may consist of subsequent refinements of specifications in the form of constraint sets. The paper presents a worked out example of dynamic service generation - such as necessary on Grids - by exploiting STROBE Agents equipped with a nondeterministic interpreter. It shows how enabling dynamic specification of a problem. Then it illustrates how these principles could be effective for other applications. Details of the implementation are not provided here, but are available.