In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants' NL input. We demonstrate that our approach is effective at learning models on these tasks.

Before engaging in a group venture agents may seek commitments from other members in the group and, based on the level of participation (i.e. the number of actually committed participants), decide whether it is worth joining the venture. Alternatively, agents can delegate this costly process to a (beneficent or non-costly) third-party, who helps seek commitments from the agents. Using methods from Evolutionary Game Theory, this paper shows that, in the context of Public Goods Game, much higher levels of cooperation can be achieved through such centralized commitment management. It provides a more efficient mechanism for dealing with commitment free-riders, those who are not willing to bear the cost of arranging commitments whilst enjoying the benefits provided by the paying commitment proposers. We show that the participation level plays a crucial role in the decision of whether an agreement should be formed; namely, it needs to be more strict in terms of the level of participation required from players of the centralized system for the agreement to be formed; however, once it is done right, it is much more beneficial in terms of the level of cooperation and social welfare achieved. In short, our analysis provides important insights for the design of multi-agent systems that rely on commitments to monitor agents' cooperative behavior.

Domain model acquisition is the problem of learning the structure of a state-transition system from some input data, typically example transition sequences. Recent work has shown that it is possible to learn action costs of PDDL models, given the overall costs of individual plans. In this work we have explored the extension of these ideas to narrative planning where cost can represent a variety of features (e.g. tension or relationship strength) and where exact solutions don’t exist. Hence in this paper we generalise earlier results to show that when an exact solution does not exist, a best-fit costing can be generated. This approach is of particular interest in the context of plan-based narrative generation where the input cost functions are based on subjective input. In order to demonstrate the effectiveness of the approach, we have learnt models of narratives using subjective measures of narrative tension. These were obtained with narratives (presented as video in this case) that were encoded as action traces, and then scored for subjective narrative tension by viewers. This provided a collection of input plan traces for our domain model acquisition system to learn a best-fit model. Using this learnt model we demonstrate how it can be used to generate new narratives that fit different target levels of tension.

This paper addresses the challenge of automated numeric domain model acquisition from observations. Many industrial and commercial applications of planning technology rely on numeric planning models. For example, in the area of autonomous systems and robotics, an autonomous robot often has to reason about its position in space, power levels and storage capacities. It is essential for these models to be easy to construct. Ideally, they should be automatically constructed. Learning the structure of planning domains from observations of action traces has produced successful results in classical planning. In this work, we present the first results in generalising approaches from classical planning to numeric planning. We restrict the numeric domains to those that include fixed action costs. Taking the finite state automata generated by the LOCM family of algorithms, we learn costs associated with machines; specifically to the object transitions and the state parameters. We learn action costs from action traces (with only the final cost of the plans as extra information) using a constraint programming approach. We demonstrate the effectiveness of this approach on standard benchmarks.

Social punishment, whereby cooperators punish defectors, has been suggested as an important mechanism that promotes the emergence of cooperation or maintenance of social norms in the context of the one-shot (i.e. non-repeated) interaction. However, whenever antisocial punishment, whereby defectors punish cooperators, is available, this antisocial behavior outperforms social punishment, leading to the destruction of cooperation. In this paper, we use evolutionary game theory to show that this antisocial behavior can be efficiently restrained by relying on prior commitments, wherein agents can arrange, prior to an interaction, agreements regarding posterior compensation by those who dishonor the agreements. We show that, although the commitment mechanism by itself can guarantee a notable level of cooperation, a significantly higher level is achieved when both mechanisms, those of proposing prior commitments and of punishment, are available in co-presence. Interestingly, social punishment prevails and dominates in this system as it can take advantage of the commitment mechanism to cope with antisocial behaviors. That is, establishment of a commitment system helps to pave the way for the evolution of social punishment and abundant cooperation, even in the presence of antisocial punishment.

We know too that guilt may be alleviated by private confession Theorists conceive of shame and guilt as belonging to the (namely to a priest or a psychotherapist) plus the family of self-conscious emotions (Lewis 1990) (Fischer renouncing of past failings in future. Because of their private and Tangney 1995) (Tangney and Dearing 2002), invoked character, such confessions and atonements, given through self-reflection and self-evaluation. Though both their cost (prayers or fees), render temptation defecting less have evolved to promote cooperation, guilt and shame can probable. Public or open confession of guilt can be coordinated be treated separately. Guilt is an inward private phenomenon, with apology for better effect, and the cost appertained though it can promote apology, and even spontaneous to some common good (like charity), or as individual public confession. Shame is inherently public, though it compensation to injured parties.

We report here on our previous research on the evolution of commitment behaviour in the one-off and iterated prisoner's dilemma and relate it to the issue of designing non-human autonomous online systems. We show that it was necessary to introduce an apology/forgiveness mechanism in the iterated case since without this restorative mechanism strategies evolve that take revenge when the agreement fails. As before in online interaction systems, apology and forgiveness seem to provide important mechanisms to repair trust. As such, these result provide, next to the insight into our own moral and ethical considerations, ideas into how (and also why) similar mechanisms can be designed into the repertoire of actions that can be taken by non-human autonomous agents.

Public goods, like food sharing and social health systems, may prosper when prior agreements to contribute are feasible and all participants commit to do so. Yet, free-riders may exploit such agreements, requiring then committers to decide whether to enact the public good when others do not commit. So deciding removes all benefits from free-riders but also from those who are willing to establish the beneficial resource. Here we discuss our work wherein we show, within the framework of the one-shot Public Goods Game (PGG) and using methods of Evolutionary Game Theory (EGT), that (i) implementing extra measures, delimiting benefits to free-riders, often leads to more favorable societal outcomes, especially in larger groups and highly beneficial public goods situations, even if so doing is costlier, and (ii) when restriction mechanism is not available, participation level (i.e. how many other players commit to the PGG cooperation) plays a crucial role in the decision making of commitment proposers, for their survival as well as for promoting the emergence of cooperation. Hence, there exist ethical fine tunings to be observed whenever establishing PGGs, be they for humans or non-humans, for otherwise the supporting joint moral ground may escape from under everyone’s feet.

The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.

The AAAI-15 organizing committee of about 60 researchers arranged many of the traditional AAAI events, including the Innovative Applications of Artificial Intelligence (IAAI) Conference, tutorials, workshops, the video competition, senior member summary talks (on well-developed bodies of research or important new research areas), and What's Hot talks (on research trends observed in other AIrelated conferences and, for the first time, competitions). Innovations of AAAI-15 included software and hardware demonstration programs, a virtual agent exhibition, a computer-game showcase, a funding information session with program directors from different funding agencies, and Blue Sky Idea talks (on visions intended to stimulate new directions in AI research) with awards funded by the CRA Computing Community Consortium. Seven invited talks surveyed AI research in academia and industry and its impact on society. Attendees kept track of the program through a smartphone app as well as social media channels.