Model update is a formal approach to correct a system model M w.r.t some property not satisfied by M. In this work, we show how this formal approach can be used for plan and planning domain verification and update. While a model checking method can directly be used to perform plan verification, model update techniques can be used to either update an incorrect plan and\or update a planning domain specification. Well known model update approaches are based on CTL — a logic which does not take into account the actions. In previous work, we have proposed the alpha-CTL logic, a logic whose semantics is based on actions. Here, we are proposing a model update system based on alpha-CTL which is able to automatically modify a plan M, generating a new plan M' that satisfies phi or, if there is not such a plan, to automatically update the corresponding planning domain.

Web Service (WS) domains constitute an application field where automated planning can significantly contribute towards achieving customisable and adaptable compositions. Following the vision of using domain-independent planning and declarative complex goals to generate compositions based on atomic service descriptions, we apply a planning framework based on Constraint Satisfaction techniques to a domain consisting of WSs with diverse functionalities. One of the key requirements of such domains is the ability to address the incomplete knowledge problem, as well as recovering from failures that may occur during execution. We propose an algorithm for interleaving planning, monitoring and execution, where continual planning via altering the CSP is performed, under the light of the feedback acquired at runtime. The system is evaluated against a number of scenarios including real WSs, demonstrating the leverage of situations that can be effectively tackled with respect to previous approaches.

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

In this paper we examine the general problem of generating preferred explanations for observed behavior with respect to a model of the behavior of a dynamical system. This problem arises in a diversity of applications including diagnosis of dynamical systems and activity recognition. We provide a logical characterization of the notion of an explanation. To generate explanations we identify and exploit a correspondence between explanation generation and planning. The determination of good explanations requires additional domain-specific knowledge which we represent as preferences over explanations. The nature of explanations requires us to formulate preferences in a somewhat retrodictive fashion by utilizing Past Linear Temporal Logic. We propose methods for exploiting these somewhat unique preferences effectively within state-of-the-art planners and illustrate the feasibility of generating (preferred) explanations via planning.

Logic, and declarative representation of knowledge in general, have long been a preferred framework for problem solving in AI. However, specific subareas of AI have been eager to abandon general-purpose knowledge representation in favor of methods that seem to address their computational core problems better. In planning, for example, state-space search has in the last several years been preferred to logic-based methods such as SAT. In our recent work, we have demonstrated that the observed performance differences between SAT and specialized state-space search methods largely go back to the difference between a blind (or at least planning-agnostic) and a planning-specific search method. If SAT search methods are given even simple heuristics which make the search goal-directed, the efficiency differences disappear.

One of the most ubiquitous AI applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, time-varying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traffic. We have applied our algorithm to large-scale traffic route planning, and demonstrated that our self-aware route planner can more accurately predict future traffic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm.

Let h be a heuristic function selected for expansions when GBFS with the FF heuristic that estimates the distance to a goal from a node n. GBFS (Hoffmann and Nebel 2001) solves a planning problem. The selects the best node n with the smallest h(n) in the open list horizontal axis indicates each expansion of the best node that maintains nodes that have been generated but have not n in the open list and the vertical axis represents n's corresponding been expanded yet. It then expands n to generate n's successors, heuristic value for that expansion. Circles, the and saves these successors in the open list, unless triangle, and diamond represent expanding nodes that are they have been previously added to the open list.

Agents in highly dynamic adversarial domains, such as RTS games, must continually make time-critical decisions to adapt their behaviour to the changing environment. In such a context, the planning agent must consider his opponent's actions as uncontrollable, or at best influenceable. In general nondeterministic domains where there is no clear turn-taking protocol, most heuristic search methods to date do not explicitly reason about the opponent's actions when guiding the state space exploration towards goal or high-reward states. In contrast, we are investigating a domain-independent heuristic planning approach which reasons about the dynamics and uncontrollability of the opponent's behaviours in order to provide better guidance to the search process of the planner. Our planner takes as input the opponent's behaviours recognized by a plan recognition module and uses them to identify opponent's actions that lead to low-utility projected states. We believe such explicit heuristic reasoning about the potential behaviours of the opponent is crucial when planning in adversarial domains, yet is missing in today's planning approaches.

We consider a new class of planning problems involving a set of non-negative real variables, and a set of non-deterministic actions that increase or decrease the values of these variables by some arbitrary amount. The formulas specifying the initial state, goal state, or action preconditions can only assert whether certain variables are equal to zero or not. Assuming that the state of the variables is fully observable, we obtain two results. First, the solution to the problem can be expressed as a policy mapping qualitative states into actions, where a qualitative state includes a Boolean variable for each original variable, indicating whether its value is zero or not. Second, testing whether any such policy, that may express nested loops of actions, is a solution to the problem, can be determined in time that is polynomial in the qualitative state space, which is much smaller than the original infinite state space. We also report experimental results using a simple generate-and-test planner to illustrate these findings.

MAPP has been previously shown as a state-of-the-art multi-agent path planning algorithm on criteria including scalability and success ratio (i.e., percentage of solved units) on realistic game maps. MAPP further provides a formal characterization of problems it can solve, and low-polynomial upper bounds on the resources required. However, until now, MAPP's solution quality had not been extensively analyzed. In this work we empirically analyze the quality of MAPP's solutions, using multiple quality criteria such as the total travel distance, the makespan and the sum of actions (including move and wait actions). We also introduce enhancements that improve MAPP's solution quality significantly. For example, the sum of actions is cut to half on average. The improved MAPP is competitive in terms of solution quality with FAR and WHCA*, two successful algorithms from the literature, and maintains its advantages on different performance criteria, such as scalability, success ratio, and ability to tell apriori if it will succeed in the instance at hand. As optimal algorithms have limited scalability, evaluating the quality of the solutions provided by suboptimal algorithms is another important topic. Using lower bounds of optimal values, we show that MAPP's solutions have a reasonable quality. For example, MAPP's total travel distance is on average 19% longer than a lower bound on the optimal value.