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One of the most remarkable things about the human moral mind is its flexibility. We can make moral judgments about cases we have never seen before. We can decide that pre-established rules should be broken. We can invent novel rules on the fly. Capturing this flexibility is one of the central challenges in developing AI systems that can interpret and produce human-like moral judgment. This paper details the results of a study of real-world decision makers who judge whether it is acceptable to break a well-established norm: ``no cutting in line.'' We gather data on how human participants judge the acceptability of line-cutting in a range of scenarios. Then, in order to effectively embed these reasoning capabilities into a machine, we propose a method for modeling them using a preference-based structure, which captures a novel modification to standard ``dual process'' theories of moral judgment.

Supplementary methods, tables and figures.

The "small world phenomenon," popularized by Stanley Milgram, suggests that individuals from across a social network are connected via a short path of mutual friends and can leverage their local social information to efficiently traverse that network. Existing social search experiments are plagued by high rates of attrition, which prohibit comprehensive study of social search. We investigate this by conducting a small world experiment at Burning Man, an event located in the Black Rock Desert of Nevada, USA, known its unique social systems and community participation. We design location-tracking vessels that we routed through Burning Man towards the goal of finding a particular person. Along the way, the vessels logged individual information and GPS data. Two of fifteen vessels made it to their designated people, but a month after Burning Man. Our results suggest possible improvements to limit rates of attrition through community participation and a design methodology that emphasizes cultural practices to aid social experimentation., Comment: 13 pages, 5 figures

This paper briefly describes AIF-EL, an OWL2-EL compliant ontology for the Argument Interchange Format. Publisher PDF

This paper briefly describes AIF-EL, an OWL2-EL compliant ontology for the Argument Interchange Format.

We model robust procurement as an optimization problem. We show that its decision version is NP-complete, and propose a backtracking algorithm with cuts that reduce the search-space to find the optimal solution. We then develop a mechanism that motivates agents to truthfully report their private information (i.e., truthful in dominant strategies), maximizes the social welfare (efficient), and ensures non-negative utilities of the participating agents even after execution (post-execution individually rational). In the experiments, we compare our mechanism with an iterated greedy first-price mechanism that represents the current practice in public procurements, in terms of the expected social welfare and the expected payments of the auctioneer. The results show that in terms of social welfare, our mechanism outperforms the greedy approach in all cases except when there exist cheap and reliable agents who can finish the job in time. In terms of payments, our mechanism outperforms the current practice when there are many potential contractors and the optimization constraints are tight.

Work on argumentation-based dialogue systems often assumes that the adoption of argumentation leads to improved dialogue efficiency and effectiveness. Several studies have taken an experimental approach to prove these alleged benefits, but none has yet supported the expressiveness of a structured argumentation logic. This paper shows how the use of argumentation in deliberation style dialogues can be tested while supporting goal-based agents that use the ASPIC framework for structured argumentation. It is experimentally shown that employing an arguing strategy increases the effectiveness over a non-argumentative strategy.

While argumentation-based negotiation has been accepted as a promising alternative to game-theoretic or heuristic based negotiation, no evidence has been provided to con¯rm this theoretical advantage. We propose a model of bilateral negotiation extending a simple monotonic concession protocol by allowing the agents to exchange information about their underlying interests and possible alternatives to achieve them during the negotiation. We present an empirical study that demonstrates (through simulation) the advantages of this interest-based negotiation approach over the more classic monotonic concession approach to negotiation.

Argumentation-based negotiation (ABN) provides agents with an effective means to resolve conflicts within a multi-agent society. However, to engage in such argumentative encounters the agents require the ability to generate arguments, which, in turn, demands four fundamental capabilities: a schema to reason in a social context, a mechanism to identify a suitable set of arguments, a language and a protocol to exchange these arguments, and a decision making functionality to generate such dialogues. This paper focuses on the first two issues and formulates models to capture them. Specifically, we propose a coherent schema, based on social commitments, to capture social influences emanating from the roles and relationships of a multi-agent society. After explaining how agents can use this schema to reason within a society, we then use it to identify two major ways of exploiting social influence within ABN to resolve conflicts. The first of these allows agents to argue about the validity of each other’s social reasoning, whereas the second enables agents to exploit social influences by incorporating them as parameters within their negotiation. For each of these, we use our schema to systematically capture a comprehensive set of social arguments that can be used within a multi-agent society.