A Formal Approach to Agent planning with inference trees

1999

Abstract In this article, I describe several challenges facing the integration of two distinct lines of AI research:(1) decision-theoretic planning (DTP) and (2) multiagent systems. Both areas (especially the second) are attracting considerable interest, but work in multiagent systems often assumes either classical planning models or prespecified economic valuations on the part of the agents in question.

2000

Abstract We introduce a computational framework, consisting of resources, skills, goals and services to represent the plans of individual agents and to develop models and algorithms for cooperation processes between a collection of agents. Keywords: Teamwork and cooperation, multiagent planning, distributed resource allocation.

2007

General Game playing, a relatively new field in game research, presents new frontiers in building intelligent game players. The traditional premise for building a good artificially intelligent player is that the game is known to the player and pre-programmed to play accordingly. General game players challenge game programmers by not identifying the game until the beginning of game play. In this paper we explore a new approach to intelligent general game playing employing a self-organizing, multiple-agent evolutionary learning strategy. In order to decide on an intelligent move, specialized agents interact with each other and evolve competitive solutions to decide on the best move, sharing the learnt experience and using it to train themselves in a social environment. In an experimental setup using a simple board game, the evolutionary agents employing a learning strategy by training themselves from their own experiences, and without prior knowledge of the game, demonstrate to be as effective as other strong dedicated heuristics. This approach provides a potential for new intelligent game playing program design in the absence of prior knowledge of the game at hand.

Expert Systems with Applications, 2012

Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent's preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.

ArXiv, 2018

The field of Game Theory provides a useful mechanism for modeling many decision-making scenarios. In participating in these scenarios individuals and groups adopt particular strategies, which generally perform with varying levels of success. However, most results have focussed on players that play the same game in an iterated fashion. This paper describes a framework which can be used to observe agents when they do not know in advance which game they are going to play. That is, the same group of agents could first play a few rounds of the Iterated Prisoner's Dilemma, and then a few rounds of the Linear Public Goods Game, and then a few rounds of Minority Game, or perhaps all games in a strictly alternating fashion or a randomized instantiation of games. This framework will allow for investigation of agents in more complex settings, when there is uncertainty about the future, and limited resources to store strategies.

We present a m ulti-agent approach to planning for con guration, repair and maintenance activities in networked computing environments. In this setting, several planners, represented by di erent agents, cooperate on the network in order to build plans of actions needed to achieve g i v en goals. The knowledge domain is partitioned in sub-domains and each agent i s i n c harge of managing and operating upon a given subset of resources, called the agent domain. In order to implement such distributed planning architecture we h a ve applied the agent t e c hnology to a constraint-based planner called PlanNet (Planning for the Network) 1]. The exploitation of the agent t e c hnology allows for an e cient and reliable distributed planning activity.

2007

Abstract One goal of Artificial Intelligence is to enable the creation of robust, fully autonomous agents that can coexist with us in the real world. Such agents will need to be able to learn, both in order to correct and circumvent their inevitable imperfections, and to keep up with a dynamically changing world. They will also need to be able to interact with one another, whether they share common goals, they pursue independent goals, or their goals are in direct conflict.

Lecture Notes in Computer Science, 2001

Applied Sciences

In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the ...

Journal of Global Research in Computer Science, 2013

The system multi-agent (SMA) is a compound system of a set agents, situated ina certain environment and interacting to certain relations. The theory of the games is a formalism which aims at studying the interactions between agents knowing that such interactions can extend from the cooperation to the conflict. The game decision making in a system multi-agent require the players to manage a high number of units placed in a very sophisticated environment where complex real-world problems are posed and state of the artificial intelligence techniques are ineffective. This article describes the problem of game decision making in multi-agent systems. Then we present the game model. After that, we propose the formalization of the game problem. Finally, we quote the methods for solving the game problem.