The autonomous intelligent system

Fritz, Walter; Martínez, Ramón García; Blanqué, Javier R.; Adobbati, Rogelio; Rama, Alberto; Sarno, Mario

doi:10.1016/0921-8890(89)90004-3

Cited by 23 publications

(28 citation statements)

References 3 publications

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“…We compared four versions of the system: -The base planner, in which operators are created directly from the observations, following Fritz et al work (Fritz et al, 1989). This conf guration Figure 14.…”

Section: Results On Learning and Planningmentioning

confidence: 99%

“…In previous work of the authors (García-Martínez, 1997), the representation of operators was based on the model proposed in (Fritz et al, 1989), in which an observation (also called experience unit) had the following structure:…”

Section: Representationmentioning

confidence: 99%

“…Autonomous intelligent behavior is an area with an emerging interest within Artif cial Intelligence researchers (Fritz et al, 1989;Mahavedan and Connell, 1992;Simmons and Mitchell, 1989;Stone and Veloso, 1998;Matellán et al, 1998;Ashish et al, 1997), ranging from work on autonomous robotic agents to Web-based software agents. It integrates many areas, such as robotics, planning, and machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is interesting to study how an autonomous system can automatically build planning operators that model its environment (Fritz et al, 1989;García-Martínez, 1993;García-Martínez, 1997). In this context, machine learning applied to planning has mainly focused on learning control knowledge in many different ways such as: macrooperators (Fikes et al, 1972), control rules (Minton, 1988;Borrajo and Veloso, 1997), control knowledge into operator descriptions (Langley, 1983) or cases (Veloso, 1994).…”

Section: Introductionmentioning

confidence: 99%

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García-Martínez

Borrajo

2000

Journal of Intelligent and Robotic Systems

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Abstract. Agents (hardware or software) that act autonomously in an environment have to be able to integrate three basic behaviors: planning, execution, and learning. This integration is mandatory when the agent has no knowledge about how its actions can affect the environment, how the environment reacts to its actions, or, when the agent does not receive as an explicit input, the goals it must achieve. Without an "a priori" theory, autonomous agents should be able to self-propose goals, set-up plans for achieving the goals according to previously learned models of the agent and the environment, and learn those models from past experiences of successful and failed executions of plans. Planning involves selecting a goal to reach and computing a set of actions that will allow the autonomous agent to achieve the goal. Execution deals with the interaction with the environment by application of planned actions, observation of resulting perceptions, and control of successful achievement of the goals. Learning is needed to predict the reactions of the environment to the agent actions, thus guiding the agent to achieve its goals more effi iently.In this context, most of the learning systems applied to problem solving have been used to learn control knowledge for guiding the search for a plan, but few systems have focused on the acquisition of planning operator descriptions. As an example, currently, one of the most used techniques for the integration of (a way of) planning, execution, and learning is reinforcement learning. However, they usually do not consider the representation of action descriptions, so they cannot reason in terms of goals and ways of achieving those goals.In this paper, we present an integrated architecture, LOPE, that learns operator def nitions, plans using those operators, and executes the plans for modifying the acquired operators. The resulting system is domain-independent, and we have performed experiments in a robotic framework. The results clearly show that the integrated planning, learning, and executing system outperforms the basic planner in that domain.

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Section: Results On Learning and Planningmentioning

confidence: 99%

Section: Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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García-Martínez

Borrajo

2000

Journal of Intelligent and Robotic Systems

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“…Usually, these learned theories are partial, incomplete and incorrect, but they can be used to plan, to further modify those theories, or to create new ones. Previous work on machine learning applied to problem solving has mainly focused on learning knowledge whose goal was to improve the efficiency of the problem solving task [1]. There is also a current interest in learning state transition probabilities in the context of reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Learning Life Cycle in Autonomous Intelligent Systems

Ierache

García-Martínez

Giusti³

Artificial Intelligence in Theory and Practice II

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Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

Sichman¹,

Coelho²,

Rezende³

2006

Lecture Notes in Computer Science

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the 9th Brazilian Neural Networks Symposium). This decision was a consequence of the successful event organized in 2000, when the First International Joint Conference IBERAMIA/ SBIA 2000 (7th Ibero-American Artificial Intelligence Conference and 15th Brazilian Artificial Intelligence Symposium) occurred in Brazil. Moreover, in 2006 the artificial intelligence community celebrated the golden anniversary of the 1956 Dartmouth Conference that marked the beginning of artificial intelligence as a research field.SBIA 2006 was the 18 th conference of the SBIA conference series, which is the leading Brazilian conference for the presentation of AI research and applications. Since 1995, SBIA has become an international conference, with papers written in English, an international Program Committee, and proceedings published in Springer's Lecture Notes in Artificial Intelligence (LNAI) series.IBERAMIA 2006 was the 10 th conference of the IBERAMIA conference series, which has been one of the most suitable forums for Ibero-American AI researchers (from South and Central American countries, Mexico, Spain and Portugal) to present their results. Following the SBIA and EPIA (Portuguese Conference on AI) experiences, from IBERAMIA 1998 on, it has also become an international conference with proceedings published in Springer's LNAI series.The International Joint Conference was held in Ribeirão Preto, Brazil, during October 23-27, 2006. The call for papers was very successful. In the registration phase, when the authors were asked to submit just the title and abstract of their papers, 281 submissions were received. In the second phase, 246 authors uploaded their full paper to be reviewed. These submissions came from 23 different countries 1 , as shown in Table 1. After the revision process, 62 papers were accepted to be published in these proceedings.In order to improve the overall quality of this joint conference, a double-blind reviewing procedure was adopted, and the acceptance rate was fixed around 25% of the submitted papers. A large group of reviewers from all content areas was set up to carry out the difficult and challenging work of selecting the papers. Every paper was evaluated by at least three reviewers, and ranked by the JEMS system. Whenever conflicts were found, open discussions among the reviewers were triggered and analyzed later to support our final decision.The conference included keynote speeches, introductory and advanced tutorials by world-leading researchers, several workshops covering specific topics and a thesis and dissertation contest.

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The autonomous intelligent system

Cited by 23 publications

References 3 publications

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Learning Life Cycle in Autonomous Intelligent Systems

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

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