Munoz-Avila, Hector


Generating Diverse Plans Using Quantitative and Qualitative Plan Distance Metrics

AAAI Conferences

Diversity-aware planning consists of generating multiple plans which, while solving the same problem, are dissimilar from one another. Quantitative plan diversity is domain-independent and does not require extensive knowledge-engineering effort, but can fail to reflect plan differences that are relevant to users. Qualitative plan diversity is based on domain-specific characteristics, thus being of greater practical value, but may require substantial knowledge engineering. We demonstrate a domain-independent diverse plan generation method that is based on customizable plan distance metrics and amenable to both quantitative and qualitative diversity. Qualitative plan diversity is obtained with minimal knowledge-engineering effort, using distance metrics which incorporate domain-specific content.


Integrated Learning for Goal-Driven Autonomy

AAAI Conferences

Goal-driven autonomy (GDA) is a reflective model of goal reasoning that controls the focus of an agent’s planning activities by dynamically resolving unexpected discrepancies in the world state, which frequently arise when solving tasks in complex environments. GDA agents have performed well on such tasks by integrating methods for discrepancy recognition, explanation, goal formulation, and goal management. However, they require substantial domain knowledge, including what constitutes a discrepancy and how to resolve it. We introduce LGDA, a learning algorithm for acquiring this knowledge, modeled as cases, that and integrates case-based reasoning and reinforcement learning methods. We assess its utility on tasks from a complex video game environment. We claim that, for these tasks, LGDA can significantly outperform its ablations. Our evaluation provides evidence to support this claim. LGDA exemplifies a feasible design methodology for deployable GDA agents.


The Special Issue of AI Magazine on Structured Knowledge Transfer

AI Magazine

This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the general problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.


The IJCAI-09 Workshop on Learning Structural Knowledge From Observations (STRUCK-09)

AI Magazine

The IJCAI-09 Workshop on Learning Structural Knowledge From Observations (STRUCK-09) took place as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) on July 12 in Pasadena, California. The workshop program included paper presentations, discussion sessions about those papers, group discussions about two selected topic and a joint discussion.


Learning Hierarchical Task Networks for Nondeterministic Planning Domains

AAAI Conferences

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 ND-HTN-Maker, that exploits these properties.  We implemented ND-HTN-Maker in the recently-proposed HTN-Maker system, a goal-regression based HTN learning approach.  In our theoretical study, we show that ND-HTN-Maker 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.


Automatically Generating Game Tactics through Evolutionary Learning

AI Magazine

Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically. Experimental results show that ESTG improves dynamic scripting's performance in a real-time strategy game. We conclude that high-quality domain knowledge can be automatically generated for strong adaptive game AI opponents.


Automatically Generating Game Tactics through Evolutionary Learning

AI Magazine

The decision-making process of computer-controlled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to ix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. In previous work, the tactics used by dynamic scripting were designed manually. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically. Experimental results show that ESTG improves dynamic scripting's performance in a real-time strategy game. We conclude that high-quality domain knowledge can be automatically generated for strong adaptive game AI opponents. Game developers can bene it from applying ESTG, as it considerably reduces the time and effort needed to create adaptive game AI.


The Sixth International Conference on Case-Based Reasoning (ICCBR-05)

AI Magazine

The Sixth International Conference on Case-Based Reasoning (ICCBR-05) took place from 23 August through 26 August 2005 at the downtown campus of De- Paul University, in the heart of Chicago's downtown Loop. The conference program included Industry Day, four workshops, and two days of technical paper presentations divided into poster sessions and a single plenary track. This report describes the conference in detail.


The Sixth International Conference on Case-Based Reasoning (ICCBR-05)

AI Magazine

The Sixth International Conference on Case-Based Reasoning (ICCBR-05) took place from 23 August through 26 August 2005 at the downtown campus of De- Paul University, in the heart of Chicago's downtown Loop. The conference program included Industry Day, four workshops, and two days of technical paper presentations divided into poster sessions and a single plenary track. This report describes the conference in detail.


Case-Based Reasoning Integrations

AI Magazine

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with modelbased reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems.