A la recherche d'une planification plus humaine

International audiencePlanning is the task of searching an action plan to achieve a goal. Classical planning is based on a set of assumptions which makes it possible to solve optimally some complex problems. They are composed of a huge number of instances, and that implies a larger search graph. However , in real-world environment this approach is less efficient. We think a human planning would be preferable for solving problems in dynamic environments in a satisfying way. It results in a need of a common-sense knowledge and thus a more expressive memory. This work aims at improving planning in real-world contexts like robotics or simulations .La planification est la recherche d'un plan d'actions afin d'atteindre un objectif. La planification classique est ba-sée sur un ensemble d'hypothèses qui permet une résolu-tion optimale de problèmes complexes. Ils sont notamment composés d'un grand nombre d'instances, ce qui implique un élargissement du graphe de recherche. Cependant, elle est moins efficace dans des environnements plus proches du monde réel. Nous pensons qu'il est préférable d'avoir une planification plus humaine, avec une résolution satisfai-sante de problèmes dans des environnements dynamiques. Il en résulte un besoin d'utiliser des connaissances géné-rales de sens commun et donc d'avoir une mémoire plus expressive. Ces travaux permettraient d'améliorer la pla-nification dans des contextes du monde réel comme dans la robotique ou la simulation. Mots Clef Planification humaine, planification dynamique, monde réel, sens commun

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency

Development of a Response Planner Using the UCT Algorithm for Cyber Defense

A need for a quick response to cyber attacks is a prevalent problem for computer network operators today. There is a small window to respond to a cyber attack when it occurs to prevent significant damage to a computer network. Automated response planners offer one solution to resolve this issue. This work presents Network Defense Planner System (NDPS), a planner dependent on the effectiveness of the detection of the cyber attack. This research first explores making classification of network attacks faster for real-time detection, the basic function Intrusion Detection System (IDS) provides. After identifying the type of attack, learning the rewards to use in the NDPS is the second important area of this research. For NDPS to assemble the optimal plan, learning the rewards for resulting network states is critical and often depends on the preferences of the network operator. Using neural networks, the second area of this research demonstrates that capturing the preferences through samples is feasible. After training the neural network, a model can be created to obtain reward estimates. The research performed in these two areas complement the final portion of the research which is assembling the optimal plan through using the Upper Bounds on Confidence for Trees (UCT) algorithm. NDPS is implemented using the UCT algorithm which allows for quick plan formulation by searching through predicted network states based on available network actions. UCT can effectively create a plan quickly and is guaranteed to provide the optimal plan, according to rewards used, if enough time is allotted. NDPS is tested against eight random attack scenarios. For each attack scenario, the plan is polled at specific time intervals to test how quickly the optimal plan can be formulated. Results demonstrate the feasibility of NDPS to be used in real world scenarios since the optimal plans for each attack type can be formulated in real-time allowing for a rapid system response

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Tutorial on Planning Graph-Based Reachability Heuristics

■ The primary revolution in automated planning in the last decade has been the very impressive scaleup in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state-of-the-art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty. Synthesizing plans capable of achieving an agent’s goals has always been a central endeavor in AI. Considerable work has been done in the last 40 years on modeling a wide variety of plan-synthesis problems and developing multiple search regimes for driving the synthesis itself. Despite this progress, the ability to synthesize reasonable length plans under even the most stringent restrictions remained severely limited. This state of affairs has changed quite dramatically in the last decade, giving rise to planners that can routinely generate plans with hundreds of actions. This revolutionary shift in scalability can be attributed in large part to the use of sophisticated reachability heuristics to guide the planners’ search. Reachability heuristics aim to estimate the cost of a plan between the current search state and the goal state. While reachability analysis can be carried out in many different way