5 research outputs found
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations
Rubik's Cube (RC) is a well-known and computationally challenging puzzle that
has motivated AI researchers to explore efficient alternative representations
and problem-solving methods. The ideal situation for planning here is that a
problem be solved optimally and efficiently represented in a standard notation
using a general-purpose solver and heuristics. The fastest solver today for RC
is DeepCubeA with a custom representation, and another approach is with
Scorpion planner with State-Action-Space+ (SAS+) representation. In this paper,
we present the first RC representation in the popular PDDL language so that the
domain becomes more accessible to PDDL planners, competitions, and knowledge
engineering tools, and is more human-readable. We then bridge across existing
approaches and compare performance. We find that in one comparable experiment,
DeepCubeA (trained with 12 RC actions) solves all problems with varying
complexities, albeit only 78.5% are optimal plans. For the same problem set,
Scorpion with SAS+ representation and pattern database heuristics solves 61.50%
problems optimally, while FastDownward with PDDL representation and FF
heuristic solves 56.50% problems, out of which 79.64% of the plans generated
were optimal. Our study provides valuable insights into the trade-offs between
representational choice and plan optimality that can help researchers design
future strategies for challenging domains combining general-purpose solving
methods (planning, reinforcement learning), heuristics, and representations
(standard or custom)
A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors
The state identification problem seeks to identify power usage patterns of
any system, like buildings or factories, of interest. In this challenge paper,
we make power usage dataset available from 8 institutions in manufacturing,
education and medical institutions from the US and India, and an initial
un-supervised machine learning based solution as a baseline for the community
to accelerate research in this area.Comment: 6 pages, power dat
On Safe and Usable Chatbots for Promoting Voter Participation
Chatbots, or bots for short, are multi-modal collaborative assistants that
can help people complete useful tasks. Usually, when chatbots are referenced in
connection with elections, they often draw negative reactions due to the fear
of mis-information and hacking. Instead, in this paper, we explore how chatbots
may be used to promote voter participation in vulnerable segments of society
like senior citizens and first-time voters. In particular, we build a system
that amplifies official information while personalizing it to users' unique
needs transparently. We discuss its design, build prototypes with frequently
asked questions (FAQ) election information for two US states that are low on an
ease-of-voting scale, and report on its initial evaluation in a focus group.
Our approach can be a win-win for voters, election agencies trying to fulfill
their mandate and democracy at large.Comment: 7 pages, In AAAI 2023 Workshop on AI for Credible Election
Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes
Increasingly powerful Large Language Model (LLM) based chatbots, like ChatGPT
and Bard, are becoming available to users that have the potential to
revolutionize the quality of decision-making achieved by the public. In this
context, we set out to investigate how such systems perform in the personal
finance domain, where financial inclusion has been an overarching stated aim of
banks for decades. We asked 13 questions representing banking products in
personal finance: bank account, credit card, and certificate of deposits and
their inter-product interactions, and decisions related to high-value
purchases, payment of bank dues, and investment advice, and in different
dialects and languages (English, African American Vernacular English, and
Telugu). We find that although the outputs of the chatbots are fluent and
plausible, there are still critical gaps in providing accurate and reliable
financial information using LLM-based chatbots
Explainable Pathfinding for Inscrutable Planners with Inductive Logic Programming
The complexity of the solutions that artificial intelligence can learn to solve problems currently surpasses its ability to explain these solutions. In many domains, explainable solutions are a necessary condition while optimality is not. Therefore, we seek to constrain solutions to the space of solutions that can be explained to a human. To do this, we build on inductive logic programming (ILP) techniques that allow us to define robust background knowledge and inductive biases. By combining ILP with a given inscrutable planner, we are able to construct an explainable graph representing solutions to all states in the state space. This graph can then be summarized using a variety of methods such as hierarchical representations and simple if/else rules. We test our approach on Towers of Hanoi and discuss future work for applications to the Rubik’s cube