Browsing by Author "Munoz-Avila, Hector"
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Item Goal Reasoning: Papers from the ACS workshop(2013-12-14) Aha, David W.; Cox, Michael T.; Munoz-Avila, HectorThis technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic.Item IMPACTing SHOP: Foundations for integrating HTN Planning and Multi-Agency(2000-02-08) Munoz-Avila, Hector; Dix, Juergen; Nau, Dana S.; Cao, YueIn this paper we describe a formalism for integrating the SHOP HTN planning system with the IMPACT multi-agent environment. Our formalism provides an agentized adaptation of the SHOP planning algorithm that takes advantage of IMPACT's capabilities for interacting with external agents, performing mixed symbolic/numeric computations, and making queries to distributed, heterogeneous information sources (such as arbitrary legacy and/or specialized data structures or external databases). We show that this agentized version of SHOP will preserve soundness and completeness if certain conditions are met. (This technical report is the updated version of CS-TR-4085) (Also cross-referenced as UMIACS-TR-2000-02)Item Planning in a Multi-Agent Environment: Theory and Practice(2002-02-19) Dix, Juergen; Munoz-Avila, Hector; Nau, Dana S.; Zhang, LinglingWe give the theoretical foundations and empirical evaluation of a planning agent, SHOP, performing \htn planning in a multi-agent environment. SHOP is based on \ashop, an agentized 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 optimizations 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 a lot. (Also UMIACS-TR-2002-13)Item SHOP and M-SHOP: Planning with Ordered Task Decomposition(2000-06-17) Nau, Dana; Cao, Yue; Lotem, Amnon; Munoz-Avila, HectorSHOP (Simple Hierarchical Ordered Planner) and M-SHOP (Multi-task-list SHOP) are planning algorithms with the following characteristics. * SHOP and M-SHOP plan for tasks in the same order that they will later be executed. This avoids some task-interaction issues that arise in other HTN planners, making the planning algorithms relatively simple. This also makes it easy to prove soundness and completeness results. * Since SHOP and M-SHOP know the complete world-state at each step of the planning process, they can use highly expressive domain representations. For example, they can do planning problems that require Horn-clause inferencing, complex numeric computations, and calls to external programs. * In our tests, SHOP and M-SHOP were several orders of magnitude faster than Blackbox, IPP, and UMCP, and were several times as fast as TLplan. * The approach is powerful enough to be used in complex real-world planning problems. For example, we are using a Java implementation of SHOP as part of the HICAP plan-authoring system for Noncombatant Evacuation Operations (NEOs). In this paper, we describe SHOP and M-SHOP, present soundness and completeness results for them, and compare them experimentally to Blackbox, IPP, TLplan, and UMCP. The results suggest that planners that generate totally ordered plans starting from the initial state can "scale up" to complex planning problems better than planners that use partially ordered plans.