We propose an approach for solving Agent Planning Programs (APP) based on a reduction to (strong-cyclic) Fully Observable Non-Deterministic (FOND) planning. APPs represent a middle-ground between automated planning and agent-oriented programming, in which the space of possible agent behavior is "programmed" as a network of declarative goals wrt an underlying planning domain. Each transition in an APP represents a local planning problem that may need to be addressed by the agent executing the APP. APPs allow the specification of continuous goal-driven behavior in which the "next" goal is externally chosen, thus going beyond one-shot planning. Two methods have been proposed for solving APPs: a principled but inefficient LTL reactive synthesis technique; and a more efficient but arguably ad-hoc approach that relies on multiple "local" classical planning and meta-level backtracking. We demonstrate how APPs can be solved in a principled manner by developing an elegant reduction to non-deterministic planning under fairness assumptions, and show experimentally with existing FOND solvers its practical value. We also provide a new solution concept that is simpler and closer to mainstream planning than the existing one.

Re-ranking plays a crucial role in product search by reassessing products from the primary retrieval system based on specific engagement and relevance criteria. While transformer-based models like the cross encoder have advanced the relevance of ranking models in recent years, a significant challenge arises from the high latency cost associated with running a cross encoder model at runtime. This challenge becomes more pronounced in the long-tail segment, where conventional techniques like caching prove ineffective. To tackle these issues, our paper introduces a scalable framework featuring a BERT-based cross encoder model for re-ranking, deployed in the Walmart search engine. We employ strategies such as intermediate representations, operator fusion, and vectorization to improve the inference latency of the cross encoder model. Furthermore, we provide a detailed discussion on the runtime implementation, highlighting key learnings and practical tricks that ensured minimal impact on response latency during production. Finally, we present the results of online experiments, including manual evaluation and interleaving test conducted on real-world e-commerce search traffic.

Learning by observing others' experiences is a hallmark of human intelligence. While the neurocomputational mechanisms underlying observational learning are well understood, less is known about whom people prefer to learn from in the context of observational learning. One hypothesis posits that learners prefer individuals who exhibit a high degree of decision noise, 'free riding' on the costly exploration of others. An alternative hypothesis is that learners prefer individuals with low decision noise, as lower decision noise is often associated with better performance. In a preregistered experiment, we found that most participants preferred to learn from low-noise (high-performing) individuals. Furthermore, exploratory analyses revealed that participants who preferred low-noise individuals tended to rely on imitation of others' actions. These findings offer a potential computational account of how learning styles are related to partner selection in social learning.

We present an approach to non-deterministic planning under full observability via Answer Set Programming. The technique can synthesise compact policies, handle both fair and unfair actions simultaneously, and readily accommodate control knowledge and procedural domain constraints. We show that whereas compact controllers may yield sub-optimal behavior under a naive executor, optimality can be recovered under a smarter executor. The developed planner is succinct, elegant, and directly implementable, thus providing higher confidence of its correctness and ease of elaboration. Experimental results show that its performance is competitive.

Many cognitive problems people face have been shown to be computationally intractable. However, tractability is typically defined in terms of asymptotic worst-case behaviour of instances. One approach for studying typical cases of NP-complete problems is based on random instances. It has been shown that random instances of many NP-complete problems exhibit a phase transition in solvability and that hard instances tend to occur in this phase transition. Here, we characterise a phase transition in solvability for random instances of the 0-1 knapsack problem in terms of two simple instance properties. Subsequently, we show that compute time of algorithms peaks in the phase transition. Remarkably, the phase transition likewise predicts where people spend the most effort. Nevertheless, their performance decreases. This suggests that instances that are difficult for electronic computers are recognized as such by people, but the increased effort does not compensate for hardness. Given the ubiquity of the knapsack problem in every-day life, a better characterisation of the properties that make instances hard will help understand commonalities and differences in computation between human and digital computers, and to improve both decision environments (contracts, regulation) as well as human-computer interfaces.

In this work we present a novel approach to check the consistency of agent designs (prior to any implementation) with respect to the requirements specifications via automated planning. This checking is essentially a search problem which makes planning technology an appropriate solution. We focus our work on BDI agent systems and the Prometheus design methodology in order to directly compare our approach to previous work. Our experiments in more than 16K random instances prove that the approach is more effective than previous ones proposed: it achieves higher coverage, lower run-time, and importantly, can handle loops in the agent detailed design and unbounded subgoal reasoning.

Intelligent agent systems built using the BDI model of agency have grown in popularity for implementing complex systems such as UAVs, military simulations, trading agents and intelligent games. The robust and flexible behaviours that these systems afford also makes testing the ‘correctness’ of these systems a non-trivial task. Whilst the main focus on existing work has been on checking the correctness of agent-programs, in this work we present an approach to formally verify agent-based designs for a particular BDI agent design methodology. The focus is on verifying whether the detailed design of the agents conform to the requirements specification. We present a sound and complete approach, formally verifiable properties, and an evaluation with respect to time and effectiveness.