Partially Observable Markov Decision Processes with Behavioral Norms

This extended abstract discusses various approaches to the constraining of Partially Observable Markov Decision Processes (POMDPs) using social norms and logical assertions in a dynamic logic framework. Whereas the exploitation of synergies among formal logic on the one hand and stochastic approaches and machine learning on the other is gaining significantly increasing interest since several years, most of the respective approaches fall into the category of relational learning in the widest sense, including inductive (stochastic) logic programming. In contrast, the use of formal knowledge (including knowledge about social norms) for the provision of hard constraints and prior knowledge for some stochastic learning or modeling task is much less frequently approached. Although we do not propose directly implementable technical solutions, it is hoped that this work is a useful contribution to a discussion about the usefulness and feasibility of approaches from norm research and formal logic in the context of stochastic behavioral models, and vice versa

Empirical-Rational Semantics of Agent Communication

The missing of an appropriate semantics of agent communication languages is one of the most challenging issues of contemporary AI. Although several approaches to this problem exist, none of them is really suitable for dealing with agent autonomy, which is a decisive property of artificial agents. This paper introduces an observation-based approach to the semantics of agent communication, which combines benefits of the two most influential traditional approaches to agent communication semantics, namely the mentalistic (agent-centric) and the objectivist (i.e., commitment- or protocol-oriented) approach. Our approach makes use of the fact that the most general meaning of agent utterances lays in their expectable consequences in terms of agent actions, and that communications result from hidden but nevertheless rational and to some extent reliable agent intentions. In this work, we present a formal framework which enables the empirical derivation of communication meanings from the observation of rational agent utterances, and introduce thereby a probabilistic and utility-oriented perspective of social commitments

Embedding Cardinality Constraints in Neural Link Predictors

Neural link predictors learn distributed representations of entities and relations in a knowledge graph. They are remarkably powerful in the link prediction and knowledge base completion tasks, mainly due to the learned representations that capture important statistical dependencies in the data. Recent works in the area have focused on either designing new scoring functions or incorporating extra information into the learning process to improve the representations. Yet the representations are mostly learned from the observed links between entities, ignoring commonsense or schema knowledge associated with the relations in the graph. A fundamental aspect of the topology of relational data is the cardinality information, which bounds the number of predictions given for a relation between a minimum and maximum frequency. In this paper, we propose a new regularisation approach to incorporate relation cardinality constraints to any existing neural link predictor without affecting their efficiency or scalability. Our regularisation term aims to impose boundaries on the number of predictions with high probability, thus, structuring the embeddings space to respect commonsense cardinality assumptions resulting in better representations. Experimental results on Freebase, WordNet and YAGO show that, given suitable prior knowledge, the proposed method positively impacts the predictive accuracy of downstream link prediction tasks.Comment: 8 pages, accepted at the 34th ACM/SIGAPP Symposium on Applied Computing (SAC '19

A Comparative Study of Argumentation-and Proposal-Based Negotiation

Summary. Recently, argumentation-based negotiation has been proposed as an alternative to classical mechanism design. The main advantage of argumentation-based negotiation is that it allows agents to exchange complex justification positions rather than just simple proposals. Its proponents maintain that this property of argumentation protocols can lead to faster and beneficial agreements when used for complex multiagent negotiation. In this paper, we present an empirical comparison of argumentation-based negotiation to proposal-based negotiation in a strategic two-player scenario, using a game-theoretic solution as a benchmark, which requires full knowledge of the stage games. Our experiments show that in fact the argumentation-based approach outperforms the proposal-based approach with respect to the quality of the agreements found and the overall time to agreement

Interaction is Meaning: A New Model for Communication in Open Systems

We propose a new model for agent communication in open systems that is based on the principle that the meaning of communicative acts lies in their experienced consequences. A formal framework for analysing such evolving semantics is defined. An extensive analysis of example interaction processes shows that our framework allows for an assessment of several properties of the communicative conventions governing a multiagent system. Among other advantages, our framework is capable of providing a very straightforward definition of communicative conflict. Also, it allows agents to reason about the e#ects of their communicative behaviour on the structure of communicative expectations as a whole when making decisions