- Award ID(s):
- 1757207
- NSF-PAR ID:
- 10155039
- Date Published:
- Journal Name:
- Artificial intelligence
- ISSN:
- 1389-5184
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In earlier work, we introduced the framework of language-based decisions, the core idea of which was to modify Savage's classical decision-theoretic framework by taking actions to be descriptions in some language, rather than functions from states to outcomes, as they are defined classically. Actions had the form ``if psi then do phi''', where psi and phi$ were formulas in some underlying language, specifying what effects would be brought about under what circumstances. The earlier work allowed only one-step actions. But, in practice, plans are typically composed of a sequence of steps. Here, we extend the earlier framework to \emph{sequential} actions, making it much more broadly applicable. Our technical contribution is a representation theorem in the classical spirit: agents whose preferences over actions satisfy certain constraints can be modeled as if they are expected utility maximizers. As in the earlier work, due to the language-based specification of the actions, the representation theorem requires a construction not only of the probability and utility functions representing the agent's beliefs and preferences, but also the state and outcomes spaces over which these are defined, as well as a ``selection function'' which intuitively captures how agents disambiguate coarse descriptions. The (unbounded) depth of action sequencing adds substantial interest (and complexity!) to the proof.more » « less
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In this paper we develop a state transition function for partially observable multi-agent epistemic domains and implement it using Answer Set Programming (ASP). The transition function computes the next state upon an occurrence of a single action. Thus it can be used as a module in epistemic planners. Our transition function incorporates ontic, sensing and announcement actions and allows for arbitrary nested belief formulae and general common knowledge. A novel feature of our model is that upon an action occurrence, an observing agent corrects his (possibly wrong) initial beliefs about action precondition and his observability. By examples, we show that this step is necessary for robust state transition. We establish some properties of our state transition function regarding its soundness in updating beliefs of agents consistent with their observability.more » « less
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Abstract Does knowledge of other people's minds grow from concrete experience to abstract concepts? Cognitive scientists have hypothesized that infants’ first‐person experience, acting on their own goals, leads them to understand others’ actions and goals. Indeed, classic developmental research suggests that before infants reach for objects, they do not see others’ reaches as goal‐directed. In five experiments (
N = 117), we test an alternative hypothesis: Young infants view reaching as undertaken for a purpose but are open‐minded about the specific goals that reaching actions are aimed to achieve. We first show that 3‐month‐old infants, who cannot reach for objects, lack the expectation that observed acts of reaching will be directed to objects rather than to places. Infants at the same age learned rapidly, however, that a specific agent's reaching action was directed either to an object or to a place, after seeing the agent reach for the same object regardless of where it was, or to the same place regardless of what was there. In a further experiment, 3‐month‐old infants did not demonstrate such inferences when they observed an actor engaging in passive movements. Thus, before infants have learned to reach and manipulate objects themselves, they infer that reaching actions are goal‐directed, and they are open to learning that the goal of an action is either an object or a place.Highlights In the present experiments, 3‐month‐old prereaching infants learned to attribute either object goals or place goals to other people's reaching actions.
Prereaching infants view agents’ actions as goal‐directed, but do not expect these acts to be directed to specific objects, rather than to specific places.
Prereaching infants are open‐minded about the specific goal states that reaching actions aim to achieve.
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In open agent systems, the set of agents that are cooperating or competing changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were tasked with fighting wildfires, they may run out of suppressants and be temporarily unavailable to assist their peers. We consider the problem of planning in these contexts with the additional challenges that the agents are unable to communicate with each other and that there are many of them. Because an agent's optimal action depends on the actions of others, each agent must not only predict the actions of its peers, but, before that, reason whether they are even present to perform an action. Addressing openness thus requires agents to model each other's presence, which becomes computationally intractable with high numbers of agents. We present a novel, principled, and scalable method in this context that enables an agent to reason about others' presence in its shared environment and their actions. Our method extrapolates models of a few peers to the overall behavior of the many-agent system, and combines it with a generalization of Monte Carlo tree search to perform individual agent reasoning in many-agent open environments. Theoretical analyses establish the number of agents to model in order to achieve acceptable worst case bounds on extrapolation error, as well as regret bounds on the agent's utility from modeling only some neighbors. Simulations of multiagent wildfire suppression problems demonstrate our approach's efficacy compared with alternative baselines.more » « less
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null (Ed.)In Savage's classic decision-theoretic framework, actions are formally defined as functions from states to outcomes. But where do the state space and outcome space come from? Expanding on recent work by Blume, Easley, and Halpern [2006], we consider a language-based framework in which actions are identified with (conditional) descriptions in a simple underlying language, while states and outcomes (along with probabilities and utilities) are constructed as part of a representation theorem. Our work expands the role of language from that of Blume, Easley, and Halpern by using it not only for the conditions that determine which actions are taken, but also the effects. More precisely, we take the set of actions to be built from those of the form do(phi), for formulas phi in the underlying language. This presents a problem: how do we interpret the result of do(phi) when phi is underspecified (i.e., compatible with multiple states)? We answer this using tools familiar from the semantics of counterfactuals; roughly speaking, do(phi) maps each state to the ``closest'' phi-state. This notion of ``closest'' is also something we construct as part of the representation theorem; in effect, then, we prove that (under appropriate assumptions) the agent is acting as if each underspecified action is first made definite and then evaluated (i.e., by maximizing expected utility). Of course, actions in the real world are often not presented in a fully precise manner, yet agents reason about and form preferences among them all the same. Our work brings the abstract tools of decision theory into closer contact with such real-world scenarios.more » « less