Héctor Muñoz-Avila

AAAI Conference 2019 Conference Paper

Is Everything Going According to Plan? Expectations in Goal Reasoning Agents

• Héctor Muñoz-Avila
• Dustin Dannenhauer
• Noah Reifsnyder

In part motivated by topics such as agency safety, there is an increasing interest in goal reasoning, a form of agency where the agents formulate their own goals. One of the crucial aspects of goal reasoning agents is their ability to detect if the execution of their courses of actions meet their own expectations. We present a taxonomy of different forms of expectations as used by goal reasoning agents when monitoring their own execution. We summarize and contrast the current understanding of how to define and check expectations based on different knowledge sources used. We also identify gaps in our understanding of expectations.

AIJ Journal 2014 Journal Article

Learning hierarchical task network domains from partially observed plan traces

• Hankz Hankui Zhuo
• Héctor Muñoz-Avila
• Qiang Yang

Hierarchical Task Network (HTN) planning is an effective yet knowledge intensive problem-solving technique. It requires humans to encode knowledge in the form of methods and action models. Methods describe how to decompose tasks into subtasks and the preconditions under which those methods are applicable whereas action models describe how actions change the world. Encoding such knowledge is a difficult and time-consuming process, even for domain experts. In this paper, we propose a new learning algorithm, called HTNLearn, to help acquire HTN methods and action models. HTNLearn receives as input a collection of plan traces with partially annotated intermediate state information, and a set of annotated tasks that specify the conditions before and after the tasks' completion. In addition, plan traces are annotated with potentially empty partial decomposition trees that record the processes of decomposing tasks to subtasks. HTNLearn outputs are a collection of methods and action models. HTNLearn first encodes constraints about the methods and action models as a constraint satisfaction problem, and then solves the problem using a weighted MAX-SAT solver. HTNLearn can learn methods and action models simultaneously from partially observed plan traces (i. e. , plan traces where the intermediate states are partially observable). We test HTNLearn in several HTN domains. The experimental results show that our algorithm HTNLearn is both effective and efficient.

IJCAI Conference 2009 Conference Paper

• Chad Hogg
• Ugur Kuter
• Héctor Muñoz-Avila

This paper describes how to learn Hierarchical Task Networks (HTNs) in nondeterministic planning domains, where actions may have multiple possible outcomes. We discuss several desired properties that guarantee that the resulting HTNs will correctly handle the nondeterminism in the domain. We developed a new learning algorithm, called HTN-MAKERND, that exploits these properties. We implemented HTN-MAKERND in the recently-proposed HTN-MAKER system, a goalregression based HTN learning approach. In our theoretical study, we show that HTN-MAKERND soundly produces HTN planning knowledge in low-order polynomial times, despite the nondeterminism. In our experiments with two nondeterministic planning domains, ND-SHOP2, a well-known HTN planning algorithm for nondeterministic domains, significantly outperformed (in some cases, by about 3 orders of magnitude) the well-known planner MBP using the learned HTNs.

ICAPS Conference 2006 Conference Paper

Learning to Do HTN Planning

• Okhtay Ilghami
• Dana S. Nau
• Héctor Muñoz-Avila

We describe HDL, an algorithm that learns HTN domain descriptions by examining plan traces produced by an expert problem-solver. Prior work on learning HTN methods requires that all the methods' information except for their preconditions be given in advance so that the learner can learn the preconditions. In contrast, HDL has no prior information about the methods. In our experiments, in most cases HDL converged fully with no more than about 200 plan traces. Furthermore, even when HDL was given only half the plan traces it required to fully converge, it usually was able to produce HTN methods that were sufficient to solve more than 3/4 of the planning problems in the test set.

KER Journal 2005 Journal Article

Case-based planning

• Michael T. Cox
• Héctor Muñoz-Avila
• Ralph Bergmann

We briefly examine case-based planning starting with the seminal work of Hammond. Derivational analogy represents an important shift of technical emphasis that helped mature the techniques. The choice of abstraction level is equally important. We conclude by discussing theoretical underpinnings and by providing some pointers to current directions.

ICML Conference 2005 Conference Paper

Learning approximate preconditions for methods in hierarchical plans

• Okhtay Ilghami
• Héctor Muñoz-Avila
• Dana S. Nau
• David W. Aha

ICAPS Conference 2002 Conference Paper

CaMeL: Learning Method Preconditions for HTN Planning

• Okhtay Ilghami
• Dana S. Nau
• Héctor Muñoz-Avila
• David W. Aha

A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present a way to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system incrementally learn conditions for HTN methods under expert supervision. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL’s soundness, completeness, and convergence properties. We also report experimental results about its speed of convergence under different conditions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.

JELIA Conference 2002 Conference Paper

Theoretical and Empirical Aspects of a Planner in a Multi-agent Environment

• Jürgen Dix
• Héctor Muñoz-Avila
• Dana S. Nau
• Lingling Zhang

Abstract We give the theoretical foundations and empirical evaluation of a planning agent, ashop, performing HTN planning in a multi-agent environment. ashop is based on ASHOP, an agentised version of the original SHOP HTN planning algorithm, and is integrated in the IMPACT multi-agent environment. We ran several experiments involving accessing various distributed, heterogeneous information sources, based on simplified versions of noncombatant evacuation operations, NEO’s. As a result, we noticed that in such realistic settings the time spent on communication (including network time) is orders of magnitude higher than the actual inference process. This has important consequences for optimisations of such planners. Our main results are: (1) using NEO’s as new, more realistic benchmarks for planners acting in an agent environment, and (2) a memoization mechanism implemented on top of shop, which improves the overall performance considerably.

ICAPS Conference 1996 Conference Paper

Planning for Manufacturing Workpieces by Storing, Indexing and Replaying Planning Decisions

• Héctor Muñoz-Avila
• Frank Weberskirch

Planning for manufacturing workpieces is a complex task that requires the interaction of a domain-specific reasoner and a generic planning mechanism. In this paper we present an architecture for organizing the case base that is based on the information provided by a generic problem solver. A retrieval procedure is then presented that uses the information provided by the domain-specific reasoner in order to improve the accuracy of the cases retrieved. However, it is not realistic to suppose that the case retrieved will entirely fit into the new problem. We present a replay procedure to obtain a partial solution that replays not only the valid decisions taken for solving the case, but also justifica-ions of rejected decisions made during the problem solving process. As a result, those completion alternatives of the partial solution are discarded that are already known to be invalid from the case.