scholarly journals Rethinking formal models of partially observable multiagent decision making

2021 ◽  
pp. 103645
Author(s):  
Vojtěch Kovařík ◽  
Martin Schmid ◽  
Neil Burch ◽  
Michael Bowling ◽  
Viliam Lisý
2019 ◽  
Author(s):  
Mark K Ho ◽  
Fiery Andrews Cushman ◽  
Michael L. Littman ◽  
Joseph L. Austerweil

Theory of mind enables an observer to interpret others' behavior in terms of unobservable beliefs, desires, intentions, feelings, and expectations about the world. This also empowers the person whose behavior is being observed: By intelligently modifying her actions, she can influence the mental representations that an observer ascribes to her, and by extension, what the observer comes to believe about the world. That is, she can engage in intentionally communicative demonstrations. Here, we develop a computational account of generating and interpreting communicative demonstrations by explicitly distinguishing between two interacting types of planning. Typically, instrumental planning aims to control states of the physical environment, whereas belief-directed planning aims to influence an observer's mental representations. Our framework (1) extends existing formal models of pragmatics and pedagogy to the setting of value-guided decision-making, (2) captures how people modify their intentional behavior to show what they know about the reward or causal structure of an environment, and (3) helps explain data on infant and child imitation in terms of literal versus pragmatic interpretation of adult demonstrators' actions. Additionally, our analysis of belief-directed intentionality and mentalizing sheds light on the socio-cognitive mechanisms that underlie distinctly human forms of communication, culture, and sociality.


2011 ◽  
Vol 204-210 ◽  
pp. 412-417
Author(s):  
Bao Xiang Cao ◽  
Xiao Na Xia ◽  
Ji Guo Yu

Expand the self-awareness and self-(decision-making) ability of agent, unify the definition about process, resource and service, then this paper builds volunteer-oriented agent internetware service logic, and designs the formal models’ design related to corresponding volunteer computing, furthermore, researches for its method sequentially. About architecture-centric service system, it is achieved as volunteer granularities and interactive relationship. On this basis, it gets to improve transparency of architecture about business implementation and autonomous ability of analysis decision-making.


2018 ◽  
Author(s):  
◽  
Andrew R. Buck

Multicriteria decision-making problems arise in all aspects of daily life and form the basis upon which high-level models of thought and behavior are built. These problems present various alternatives to a decision-maker, who must evaluate the trade-offs between each one and choose a course of action. In a sequential decision-making problem, each choice can influence which alternatives are available for subsequent actions, requiring the decision-maker to plan ahead in order to satisfy a set of objectives. These problems become more difficult, but more realistic, when information is restricted, either through partial observability or by approximate representations. Pathfinding in partially observable environments is one significant context in which a decision-making agent must develop a plan of action that satisfies multiple criteria. In general, the partially observable multiobjective pathfinding problem requires an agent to navigate to certain goal locations in an environment with various attributes that may be partially hidden, while minimizing a set of objective functions. To solve these types of problems, we create agent models based on the concept of a mental map that represents the agent's most recent spatial knowledge of the environment, using fuzzy numbers to represent uncertainty. We develop a simulation framework that facilitates the creation and deployment of a wide variety of environment types, problem definitions, and agent models. This computational mental map (CMM) framework is shown to be suitable for studying various types of sequential multicriteria decision-making problems, such as the shortest path problem, the traveling salesman problem, and the traveling purchaser problem in multiobjective and partially observable configurations.


AI Magazine ◽  
2012 ◽  
Vol 33 (4) ◽  
pp. 82 ◽  
Author(s):  
Prashant J. Doshi

Decision making is a key feature of autonomous systems. It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations. I conclude by examining the avenues that research pertaining to these frameworks is pursuing.


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