h-approximation: History-Based Approximation of Possible World Semantics as ASP

We propose an approximation of the Possible Worlds Semantics (PWS) for action planning. A corresponding planning system is implemented by a transformation of the action specification to an Answer-Set Program. A novelty is support for postdiction wrt. (a) the plan existence problem in our framework can be solved in NP, as compared to $\Sigma_2^P$ for non-approximated PWS of Baral(2000); and (b) the planner generates optimal plans wrt. a minimal number of actions in $\Delta_2^P$. We demo the planning system with standard problems, and illustrate its integration in a larger software framework for robot control in a smart home.Comment: 12th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2013

A State-Based Regression Formulation for Domains with Sensing Actions<br> and Incomplete Information

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.Comment: 34 pages, 7 Figure

Hybrid conditional planning using answer set programming

We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains

Système d'aide à la décision pour le réseau de distribution

RÉSUMÉ De nos jours, de nouvelles technologies issues du domaine de l'information et de la communication sont introduites progressivement dans les réseaux de distribution électrique. Ces technologies nécessitent des études poussées et des simulations précises afin d'en évaluer les forces et les faiblesses. Toutefois, la simulation des réseaux électriques demeure une tâche complexe qui nécessite de tenir compte de plusieurs facteurs : électriques, mécaniques, économiques, naturels, matériels et humains. Pour pallier à la complexité inhérente à la simulation électrique, il est possible de recourir aux systèmes multiagents (SMA). Ils présentent de nombreux avantages. Ils offrent une grande flexibilité en permettant à des agents autonomes de collaborer pour atteindre des objectifs complexes. Le SMA, par opposition au système de simulation monolithique, présente l'avantage d'être une architecture souple et évolutive capable de traiter des opérations complexes. Toutefois, le développement et la manipulation de ces systèmes sont des tâches réservées à des experts en informatique et en SMA. Or, dans le cadre du projet LEOPAR, mené à l'Institut de recherche d'Hydro Québec, nous avons comme principal objectif de développer un SMA accessible à des non-experts en informatique. Le but est de permettre aux décideurs et aux ingénieurs électriques de modifier et de faire évoluer le simulateur de la manière la plus aisée possible. Pour ce faire, nous avons développée une architecture à mi-chemin entre les architectures de Tableau Noir et les SMA. Nous avons utilisé une zone distribuée de partage de données pour permettre la communication des agents. Le partage et l'échange d'informations se fait par la modification des données distribuées. Ce mécanisme réduit la complexité des agents et leur mode de communication. De plus, nous avons spécifié un langage d'actions de haut niveau qui permet de décrire de manière déclarative les actions, leurs effets, leurs conditions et leurs relations. Ce langage d'actions est automatiquement traduit en logique non monotone (Answer Set Programming) afin de permettre la coordination des agents du simulateur. La traduction que nous proposons du langage d'actions surpasse largement les autres langages d'actions en termes de rapidité d'exécution lors de la planification. La combinaison de notre langage d'actions et de la logique non monotone a permis le développement d'un système performant, qui offre la possibilité à des novices de rajouter, modifier ou supprimer des agents du simulateur. Le simulateur multiagents que nous avons développé fonctionne adéquatement et permet, entre autre, de réaliser des simulations de type Monte-Carlo pour l’étude de la fiabilité des réseaux. Notre simulateur permet de quantifier, à l'aide des indices de performances, l'impact et l'apport de nouvelles technologies. Il est en mesure de reproduire avec une grande fidélité des phénomènes électriques, mécaniques et humains, tels que la surcharge électrique des câbles, le changeur de prise des transformateurs, les équipes humaines d'intervention, le temps de restauration variable et la reconfiguration du réseau. Notre simulateur a été testé sur de véritables réseaux de distribution d'Hydro-Québec et a démontré sa capacité à traiter de grandes quantités de données. En comparaison à d'autres simulateurs électriques multiagents standards, notre système s'est avéré être tout aussi performant mais beaucoup plus facile à développer et à faire évoluer. Lors des simulations électriques, nous avons été en mesure de réaliser des études de fiabilité qui ont permis de déterminer les facteurs les plus importants influant les performances du réseau.----------ABSTRACT Nowadays, the information system technologies are increasingly used in power distribution systems to improve network reliability and performance. The impact of these structural changes is important and requires in-depth studies and investigations. A better understanding of the effect of these technologies is required to optimize the network. However, the simulation of power network is a complex task, where several technical issues need to be considered such as : electrical, mechanical, economical, natural and human aspects. The idea is to develop a multi-agent system (MAS) that can process complex simulations. Such a system is extensible and modular and it is composed by numerous simple agents that can collaborate and interact in order to achieve complex objectives. Multi-agent systems are capable of reaching goals that are difficult to achieve by monolithic systems or individual agents, which can be complex and hard to maintain and extend. Nevertheless, the development and the maintenance of a MAS is a complex task that has to be performed by experts on computer science and multi-agent systems. In the framework of the project LEOPAR, carried out by the \textit{Institute de Recherche d'Hydro-Québec}, we have as a main objective to develop an accessible and comprehensive MAS. The project's aim was to allow managers to modify the behavior and the objectives of the simulator without the assistance of an expert. To this end, we developed a simulator based on Blackboard and MAS. Our system relies on a common pool of data to share information between agents. This type of mechanism reduces the communication complexity and makes the development of agents easier. In addition, we defined a new action language that allows to incrementally describe the agent's actions, effects, conditions and relations. Our action language is automatically translated into a non-monotonic logic (Answer Set Programming) in order to process the agent's actions. The translated answer set program has shown to be effective in providing action plans. The action language combined to answer set programming allowed us to develop a powerful and accessible simulator, enabling novice to add, change, and remove agents' behavior. Our simulator works properly and allows, among other things, processing power network assessments using a Monte-Carlo approach. It analyses the impact of introducing new types of technologies, by comparing performance indicators of the network. Moreover, it is able to simulate with accuracy a wide variety of phenomena as wire overloading, protection mechanism activation, tap changer changes, human intervening team patrols, restoration process and network reconfiguration. It has been tested on realistic distribution network of Hydro-Quebec and it performed well in assessing networks. Our simulator is performing similarly to a classical multi-agents system, but with the benefit of being accessible and easy to use

Postdictive Reasoning in Epistemic Action Theory

If an agent executes an action, this will not only change the world physically, but also the agent's knowledge about the world. Therefore the occurrence of an action can be modeled as an epistemic state transition which maps the knowledge state of an agent to a successor knowledge state. For example, consider that an agent in a state s_0 executes an action a. This causes a transition to a state s_1. Subsequently, the agent executes a sensing action a_s, which produces knowledge and causes a transition to a state s_2. With the information which is gained by the sensation, the agent can not only extend its knowledge about s_2, but also infer additional knowledge about the initial state s_0. That is, the agent uses knowledge about the present to retrospectively acquire additional information about the past. We refer to this temporal form of epistemic inference as postdiction. Existing action theories are not capable of efficiently performing postdictive reasoning because they require an exponential number of state variables to represent an agent's knowledge state. The contribution of this thesis is an approximate epistemic action theory which is capable of postdictive reasoning while it requires only a linear number of state variables to represent an agent's knowledge state. In addition, the theory is able to perform a more general temporal form of postdiction, which most existing approaches do not support. We call the theory the h-approximation (HPX) because it explicitly represents historical knowledge about past world states. In addition to the operational semantics of HPX, we present its formalization in terms of Answer Set Programming (ASP) and provide respective soundness results. The ASP implementation allows us to apply HPX in real robotic applications by using off-the-shelf ASP solvers. Specifically, we integrate of HPX in an online planning framework for Cognitive Robotics where planning, plan execution and abductive explanation tasks are interleaved. As a proof-of-concept, we provide a case-study which demonstrates the application of HPX for high-level robot control in a Smart Home. The case-study emphasizes the usefulness of postdiction for abnormality detection in robotics: actions which are performed by robots are often not successful due to unforeseen practical problems. A solution is to verify action success by observing the effects of the action. If the desired effects do not hold after action execution, then one can postdict the existence of an abnormality