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Collaborating Authors

Scheutz, Matthias


Generating Justifications for Norm-Related Agent Decisions

arXiv.org Artificial Intelligence

W e present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent's rules, actions, and the extent to which the agent violated the rules) as well as "why" questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. W e use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.


Engaging in Dialogue about an Agent's Norms and Behaviors

arXiv.org Artificial Intelligence

W e present a set of capabilities allowing an agent planning with moral and social norms represented in temporal logic to respond to queries about its norms and behaviors in natural language, and for the human user to add and remove norms directly in natural language. The user may also pose hypothetical modifications to the agent's norms and inquire about their effects.


Augmenting Robot Knowledge Consultants with Distributed Short Term Memory

arXiv.org Artificial Intelligence

Human-robot communication in situated environments involves a complex interplay between knowledge representations across a wide variety of modalities. Crucially, linguistic information must be associated with representations of objects, locations, people, and goals, which may be represented in very different ways. In previous work, we developed a Consultant Framework that facilitates modality-agnostic access to information distributed across a set of heterogeneously represented knowledge sources. In this work, we draw inspiration from cognitive science to augment these distributed knowledge sources with Short Term Memory Buffers to create an STM-augmented algorithm for referring expression generation. We then discuss the potential performance benefits of this approach and insights from cognitive science that may inform future refinements in the design of our approach.


Quasi-Dilemmas for Artificial Moral Agents

arXiv.org Artificial Intelligence

In this paper we describe moral quasi-dilemmas (MQDs): situations similar to moral dilemmas, but in which an agent is unsure whether exploring the plan space or the world may reveal a course of action that satisfies all moral requirements. We argue that artificial moral agents (AMAs) should be built to handle MQDs (in particular, by exploring the plan space rather than immediately accepting the inevitability of the moral dilemma), and that MQDs may be useful for evaluating AMA architectures.


Kasenberg

AAAI Conferences

Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.


Sadeghi

AAAI Conferences

It has been suggested that early human word learning occurs across learning situations and is bootstrapped by syntactic regularities such as word order. Simulation results from ideal learners and models assuming prior access to structured syn-tactic and semantic representations suggest that it is possible to jointly acquire word order and meanings and that learning is improved as each language capability bootstraps the other.We first present a probabilistic framework for early syntactic bootstrapping in the absence of advanced structured representations, then we use our framework to study the utility of joint acquisition of word order and word referent and its onset, in a memory-limited incremental model. Comparing learning results in the presence and absence of joint acquisition of word order in different ambiguous contexts, improvement in word order results showed an immediate onset, starting in early trials while being affected by context ambiguity. Improvement in word learning results on the other hand, was hindered in early trials where the acquired word order was imperfect,while being facilitated by word order learning in future trials as the acquired word order improved. Furthermore, our results showed that joint acquisition of word order and word referent facilitates one-shot learning of new words as well as inferring intentions of the speaker in ambiguous contexts.


Early Syntactic Bootstrapping in an Incremental Memory-Limited Word Learner

AAAI Conferences

It has been suggested that early human word learning occurs across learning situations and is bootstrapped by syntactic regularities such as word order. Simulation results from ideal learners and models assuming prior access to structured syn-tactic and semantic representations suggest that it is possible to jointly acquire word order and meanings and that learning is improved as each language capability bootstraps the other.We first present a probabilistic framework for early syntactic bootstrapping in the absence of advanced structured representations, then we use our framework to study the utility of joint acquisition of word order and word referent and its onset, in a memory-limited incremental model. Comparing learning results in the presence and absence of joint acquisition of word order in different ambiguous contexts, improvement in word order results showed an immediate onset, starting in early trials while being affected by context ambiguity. Improvement in word learning results on the other hand, was hindered in early trials where the acquired word order was imperfect,while being facilitated by word order learning in future trials as the acquired word order improved. Furthermore, our results showed that joint acquisition of word order and word referent facilitates one-shot learning of new words as well as inferring intentions of the speaker in ambiguous contexts.


Norm Conflict Resolution in Stochastic Domains

AAAI Conferences

Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.



Kunze

AAAI Conferences

The ability to refer to entities such as objects, locations, and people is an important capability for robots designed to interact with humans. For example, a referring expression (RE) such as "Do you mean the box on the left?" might be used by a robot seeking to disambiguate between objects. In this paper, we present and evaluate algorithms for Referring Expression Generation (REG) in small-scale situated contexts. We first present data regarding how humans generate small-scale spatial referring expressions (REs). We then use this data to define five categories of observed small-scale spatial REs, and use these categories to create an ensemble of REG algorithms. Next, we evaluate REs generated by those algorithms and by humans both subjectively (by having participants rank REs), and objectively, (by assessing task performance when participants use REs) through a set of interrelated crowdsourced experiments. While our machine generated REs were subjectively rated lower than those generated by humans, they objectively significantly outperformed human REs. Finally, we discuss the main contributions of this work: (1) a dataset of images and REs, (2) a categorization of observed small-scale spatial REs, (3) an ensemble of REG algorithms, and (4) a crowdsourcing-based framework for subjectively and objectively evaluating REG.