28,537 research outputs found
Text-based Adventures of the Golovin AI Agent
The domain of text-based adventure games has been recently established as a
new challenge of creating the agent that is both able to understand natural
language, and acts intelligently in text-described environments.
In this paper, we present our approach to tackle the problem. Our agent,
named Golovin, takes advantage of the limited game domain. We use genre-related
corpora (including fantasy books and decompiled games) to create language
models suitable to this domain. Moreover, we embed mechanisms that allow us to
specify, and separately handle, important tasks as fighting opponents, managing
inventory, and navigating on the game map.
We validated usefulness of these mechanisms, measuring agent's performance on
the set of 50 interactive fiction games. Finally, we show that our agent plays
on a level comparable to the winner of the last year Text-Based Adventure AI
Competition
Monte Carlo Planning method estimates planning horizons during interactive social exchange
Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference
Self-Commitment-Institutions and Cooperation in Overlapping Generations Games
This paper focuses on a two-period OLG economy with public imperfect observability over the intergenerational cooperative dimension. Individual endowment is at free disposal and perfectly observable. In this environment we study how a new mechanism, we call Self-Commitment-Institution (SCI), outperforms personal and community enforcement in achieving higher ex-ante e¢ ciency. Social norms with and without SCI are characterized. If social norms with SCI are implemented, agents might freely dispose of their endowment. As long as they reduce their marginal gain from deviation in terms of current utility, they also credibly self-commit on intergenerational cooperation. Under quite general conditions we .nd that, even if individual strategies are still characterized by behavioral uncertainty, the introduction of SCI relaxes the inclination toward opportunistic behavior and sustains higher e¢ ciency compared to social norms without SCI. We quantify the value of SCI and investigate the role of memory with di¤erent social norms. Finally, applications on intergenerational public good games and transfer games with productive SCI are providedCooperation; Free disposal; Imperfect public monitoring; Memory; Overlapping generation game; Self-Commitment Institution;
Monte Carlo Planning method estimates planning horizons during interactive social exchange
Reciprocating interactions represent a central feature of all human
exchanges. They have been the target of various recent experiments, with
healthy participants and psychiatric populations engaging as dyads in
multi-round exchanges such as a repeated trust task. Behaviour in such
exchanges involves complexities related to each agent's preference for equity
with their partner, beliefs about the partner's appetite for equity, beliefs
about the partner's model of their partner, and so on. Agents may also plan
different numbers of steps into the future. Providing a computationally precise
account of the behaviour is an essential step towards understanding what
underlies choices. A natural framework for this is that of an interactive
partially observable Markov decision process (IPOMDP). However, the various
complexities make IPOMDPs inordinately computationally challenging. Here, we
show how to approximate the solution for the multi-round trust task using a
variant of the Monte-Carlo tree search algorithm. We demonstrate that the
algorithm is efficient and effective, and therefore can be used to invert
observations of behavioural choices. We use generated behaviour to elucidate
the richness and sophistication of interactive inference
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