Leliane Nunes de Barros

A library of system-derived problem-solving methods for planning

Constructing a planner for a particular application is a difficult job, for which little concrete support is currently available. The literature on planning is overwhelming and there exists no clear synthesis of the various planning methods which could be used by knowledge engineers. The contribution of this paper concerns an approach to provide concrete support for engineering planning systems. We use modern knowledge modeling approaches to analyse planning systems described in the literature. The analysis yields a detailed description of these planning systems in terms of the domain knowledge they use and the problem-solving methods they comprise. We show how the result of the analysis can be considered as a library of system-derived problem-solving methods for planning. This library consists of planning problem-solving methods along with their assumptions, which describe the applicability conditions on the domain knowledge of the methods. We describe how the library supports knowledge engineers in building planning systems and present two implemented tools based on the approach.

A logic-based agent that plans for extended reachability goals

Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic ctl turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the ctl’s semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called α-ctl. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.

Advances in Artificial Intelligence - SBIA 2012

Defeasible Logic Programming (DeLP) is a formalism able to represent incomplete and potentially contradictory information that combines logic programming with defeasible argumentation. In the past few years, this formalism has been applied to real world scenarios with encouraging results. Not withstanding, the outcome one may obtain in this or any other argumentative system is directly related to the decisions (or lack thereof) made during the phase of knowledge representation. In addition, this is exacerbated by the usual lack of a formal methodology able to assist the knowledge engineer during this critical phase. In this article, we propose a formal methodology for knowledge representation in DeLP, that defines a set of guidelines to be used during this phase. Our methodology results in an key tool to improve DeLP’s applicability to concrete domains.

Planning with Abduction: A Logical Framework to Explore Extensions to Classical Planning

In this work we show how a planner implemented as an abductive reasoning process can have the same performance and behavior as classical planning algorithms. We demonstrate this result by considering three different versions of an abductive event calculus planner on reproducing some important comparative analyses of planning algorithms found in the literature. We argue that a logic-based planner, defined as the application of general purpose theorem proving techniques to a general purpose action formalism, can be a very solid base for the research on extending the classical planning approach.

Using Concept Hierarchies in Knowledge Discovery

In Data Mining, one of the steps of the Knowledge Discovery in Databases (KDD) process, the use of concept hierarchies as a background knowledge allows to express the discovered knowledge in a higher abstraction level, more concise and usually in a more interesting format. However, data mining for high level concepts is more complex because the search space is generally too big. Some data mining systems require the database to be pre-generalized to reduce the space, what makes difficult to discover knowledge at arbitrary levels of abstraction. To efficiently induce high-level rules at different levels of generality, without pre-generalizing databases, fast access to concept hierarchies and fast query evaluation methods are needed.

On the revision of planning tasks

When a planning task cannot be solved then it can often be made solvable by modifying it a bit: one may change either the set of actions, or the initial state, or the goal description. We show that modification of actions can be reduced to initial state modification. We then apply Katsuno and Mendelzons distinction between update and revision and show that the modification of the initial state is an update and the modification of the goal description is a revision. We consider variants of Forbuss update and Dalals revision operation and argue that existing belief change operations do not apply as they stand because their inputs are boolean formulas, while plan task modification involves counterfactual statements. We show that they can be captured in Dynamic Logic of Propositional Assignments DL-PA.

Strong Probabilistic Planning

We consider the problem of synthesizing policies, in domains where actions have probabilisticeffects, that are optimal in the expected-caseamong the optimal worst-case strongpolicies. Thus we combine features from nondeterministic and probabilistic planning in a single framework. We present an algorithm that combines dynamic programming and model checking techniques to find plans satisfying the problem requirements: the strong preimage computation from model checking is used to avoid actions that lead to cycles or dead ends, reducing the model to a Markov Decision Process where all possible policies are strong and worst-case optimal (i.e., successful and minimum length with probability 1). We show that backward induction can then be used to select a policy in this reduced model. The resulting algorithm is presented in two versions (enumerative and symbolic); we show that the latter version allows planning with extended reachability goals.

Unifying nondeterministic and probabilistic planning through imprecise markov decision processes

This paper proposes an unifying formulation for nondeterministic and probabilistic planning. These two strands of AI planning have followed different strategies: while nondeterministic planning usually looks for minimax (or worst-case) policies, probabilistic planning attempts to maximize expected reward. In this paper we show that both problems are special cases of a more general approach, and we demonstrate that the resulting structures are Markov Decision Processes with Imprecise Probabilities (MDPIPs). We also show how existing algorithms for MDPIPs can be adapted to planning under uncertainty.

System design modification with actions

System designers are expected to use error-detecting and correcting techniques. Although, model checking approaches have been used for verification of errors in large complex systems, they can only detect the error. the task of correcting the system design (called model update) is completely left to the system designer. Recent works on model update can suggest changes in the system model which do not consider domain contingencies and constraints. In this paper, we present a model update approach that can be used to automatically suggest modifications in a system based on the actions that are behind state transitions and a set of domain constraints. We claim that with this approach we can develop more realistic system error-correcting tools.

Real-time dynamic programming for Markov decision processes with imprecise probabilities

Markov Decision Processes have become the standard model for probabilistic planning. However, when applied to many practical problems, the estimates of transition probabilities are inaccurate. This may be due to conflicting elicitations from experts or insufficient state transition information. The Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) was introduced to obtain a robust policy where there is uncertainty in the transition. Although it has been proposed a symbolic dynamic programming algorithm for MDP-IPs (called SPUDD-IP) that can solve problems up to 22 state variables, in practice, solving MDP-IP problems is time-consuming. In this paper we propose efficient algorithms for a more general class of MDP-IPs, called Stochastic Shortest Path MDP-IPs (SSP MDP-IPs) that use initial state information to solve complex problems by focusing on reachable states. The (L)RTDP-IP algorithm, a (Labeled) Real Time Dynamic Programming algorithm for SSP MDP-IPs, is proposed together with three different methods for sampling the next state. It is shown here that the convergence of (L)RTDP-IP can be obtained by using any of these three methods, although the Bellman backups for this class of problems prescribe a minimax optimization. As far as we are aware, this is the first asynchronous algorithm for SSP MDP-IPs given in terms of a general set of probability constraints that requires non-linear optimization over imprecise probabilities in the Bellman backup. Our results show up to three orders of magnitude speedup for (L)RTDP-IP when compared with the SPUDD-IP algorithm.